Olivier Emile

Christian Brousseau

Janine Emile

Ronan Niemiec

Kouroch Mahdjoubi

Bo Thide

Kouroch Madhjoubi

Electromagnetically induced torque on a large ring in the microwave range

Keywords: numbers: 41-20.Jb, 45-20.da, 84-40.Ba, 07.57.Kp, 42.50.Tx

Electromagnetically induced torque on a large ring in the microwave range.

1

Angular momentum exchanges generally lead to mechanical torques. On the one hand in optics, in a pioneering experiment, Beth [1] demonstrates that the spin of electromagnetic (EM) waves can induce rotation of 2 cm-diameter birefringent plates.

In his experiment he used parametric amplification to magnify the effect. This effect has since been experimentally confirmed in the macroscopic domain, both in optics [2,3] and using microwaves [4,5]. This transfer has also been experimentally evidenced in the microscopic [6] and submicroscopic scales [7,8] using optics only. On the other hand, light can also carry Orbital Angular Momentum (OAM) [9][START_REF] Allen | Optical Angular Momentum[END_REF][START_REF] Molina-Terriza | [END_REF][12]. OAM and Spin Angular Momentum (SAM) are usually considered as uncorrelated variables in the paraxial approximation [13][14][15][16]. Besides, OAM can exceed the maximum SAM of ℏ per photon. This angular momentum has been used to induce rotation of microparticles [17][18][19], since there is no need for birefringence nor absorption, of Bose-Einstein condensates [20], and even of atoms [21]. However, in the microwave domain, no direct evidence of OAM transfer from an electromagnetic wave has ever been demonstrated. The aim of this letter is thus to explore the exchange between electromagnetic microwaves carrying OAM and a large suspended ring and to investigate the induced torque effect and its consequences.

The torsion pendulum has been used for over two centuries for fundamental measurements and is still the most sensitive tool for measuring weak torques either to test the gravitational inverse square law at very small length scales [22] or to study the dynamical response of threads [23]. It has also been used in photon angular momentum transfer to a suspended object [2][3][4][5]. We also chose a torsion pendulum to evidence the transfer of OAM. Moreover, since for a given power, OAM is proportional to the wavelength, we performed experiments in the microwave regime (see Fig. 1). Actually, such a configuration has already been proposed few years ago by Vul'fson [24] but never realized. The antenna, also known as a turnstile antenna [25], is composed of two 17-cm-long, 2-mm-diameter copper dipole antennas. Such antenna is usually used to radiate circular polarized EM fields perpendicular to the antenna, however, in the plane of the antenna, it is supposed to radiate EM fields carrying ℓ = 1 OAM [24].

According to the experimental radiation pattern, we chose to perform the experiment at a frequency ν = 870 MHz. A -3dB coupler splits the monofrequency signal, generated from a frequency synthesizer in two, and induces a φ 1 -φ 2 = π/2 phase between the outputs. The signals are amplified by two 40 dB gain amplifiers and then sent to the antenna. The maximum total output is P = 25 W. The object to be rotated is a copper ring (radius R = 15 cm, height H = 5 cm, thickness T = 165 µm). Its inertial momentum is J = 8.4 10 -4 kgm 2 . The suspension is a 2 m-long, 0.5 mm-diameter cotton thread. Its torsion constant is deduced from the 12 min period of the free oscillations of the pendulum and equals C = 5. 6 10 -8 Nm/ • . To avoid any spurious EM effect, the experiment is confined in an anechoic chamber. Special care is taken to isolate the set-up from any mechanical vibration.

The copper ring is graduated and the rotation is recorded on a webcam connected to a computer.

Let us evaluate the electric and magnetic fields radiated by the turnstile antenna.

We use the cylindrical coordinates (ρ, θ, z), The origin O being at the center of the turnstile antenna, e z being perpendicular to it. However, the suspended ring doesn't lie in the far field and the whole components of the radiated fields have to be taken into account. The main results are presented here. The calculation is detailed in the Supplemental Material [26]. The complex potential vector, in the plane of the turnstile antenna, writes at a point M

A(M, t) = - µ 0 4π jω r p 0 e j(kr-ωt) e jθ (e ρ + je θ ), (1)
where r = OM , ω is the pulsation of the current, p 0 is the dipole moment, µ 0 is the magnetic permeability and k is the wave vector. The complex magnetic field then writes B(M, t) = -µ 0 4π jω r 2 p 0 (1 -jkr)e j(kr-ωt) e j(θ-π/2) e z .

(

The complex electric field writes

E(M, t) = 1 4πǫ 0 p 0 r 3 e j(kr-ωt) e jθ 2(1 -jkr)e ρ -j(1 -jkr -k 2 r 2 )e θ , (3)
ǫ 0 being the electric permittivity. One can note on this expression that, for a given distance r from the turnstile antenna, the modulus of the electric field is constant, its direction rotates around z, and, due to the e jθ term in Eq.3, its phase varies from 0 to 2π in one turn. This is in agreement with simulations and experiments that have been performed on a turnstile antenna, in a different configuration, for radial slot antennas [START_REF] Sudo | Proc. 33rd Eur. Microwave Conf[END_REF].

One could wonder whether this variation could be assigned to OAM. If one considers the far field only, one may jump to an erroneous conclusion. Indeed, in the far field, in the plane of the antenna, the polarization of the electric field is linear, there is no SAM. Considering the direction of the electric and magnetic fields, one may conclude that there is also no total angular momentum. Let us call Ω the solid angle subtended at the center of the antenna by the ring. It follows [13,26] that the total angular momentum J equals

J = µ 0 ω (4π) 2 p 2 0 r 2 Ω(k 2 r 2 + 1)e z , (4)
Curiously enough, a near field component of the electric field contributes to the total angular momentum, even in the far field. The SAM S equals

S = µ 0 ω (4π) 2 p 2 0 r 2 Ωe z , (5)
and the OAM L equals

L = J -S = µ 0 ω (4π) 2 p 2 0 Ωk 2 e z , (6)
From these equations, it turns out that the SAM has a small effect in the near field only whereas the OAM has the same and constant contribution in the near and far field. At the ring location (r = R = 15 cm), L/S = k 2 r 2 ≈ 7.5. Thus, in our case, the angular moment is mainly from OAM origin. From CST simulations [START_REF][END_REF], we have estimated that along the strip, even out of the plane of the turnstile antenna, the contribution of the OAM component to the total AM is 95% .

Fig. 2 shows the experimental rotations of the pendulum versus time for three powers together with a parabola fitting of the curve. Clearly, the movement is a uniformly accelerated rotation. This is the first direct observation of the transfer of electromagnetic OAM, to a large object. For a longer time of observation, the restoring torque of the thread is no longer negligible. The rotation depends on the power, as expected from Eq.4. As the phase between the two dipole antennas is reversed, the sign of the OAM as well as the torque changes. Indeed we observed that the rotation direction of the pendulum is changed, as expected. Besides, as can be seen in Fig. 2, the two plots of the rotation are nearly perfectly symmetric.

Of course, when the two dipole antennas are in phase, no rotation is observed. We moved the copper ring (few centimeters) with respect to the turnstile antenna as well upwards and downwards, giving the same results.

Based on Fig. 2, Fig. 3 shows the angular acceleration induced by the EM field versus power. The experimental results evidence a linear dependence on the power, as expected, that holds over one order of magnitude. Note also that the linear coefficient is exactly reversed for the other direction of rotation. From Fig. 3, for a P = 25 W power, we find an acceleration of 3.6 10 -4 • /s 2 which corresponds to an OAM torque of 5.3 10 -9 Nm. We can compare this value to the maximum angular momentum available carried by the EM fields per unit time, for a ℓ = 1 wave and a power of 25 W, Γ th = ℏN = ℏ(P/hν) = 4.6 10 -9 Nm. Here N is the number of photons emitted per second. However, due to the solid angle subtended by the ring, roughly only one sixth of the total number of photons available interacts with the ring, assuming a uniform repartition. Each photon must thus be used several times via multiple reflections. Indeed one should consider the turnstile antenna and the copper ring as a whole system which is a much more complicated case. Simulations using CST indicate that the field radiated by the antenna induces alternating currents within the strip which magnitude equals 0.15 A. Actually these currents are not exactly alternating since there is a phase shift due to the e jθ term in Eq.3. These currents are dissipated in the ring and thus induce a torque that is estimated to Γ cal = 7.0 10 -9 Nm which is in reasonable agreement with the experimental value.

Conversely, due to the very high sensitivity of the torsion pendulum, we have been able to detect an applied torque due to the transfer of OAM as low as 6.3 10 -10 Nm, corresponding to a power of 3 W (see Fig. 2). Improving the suspension mechanics of the thread [23] and inserting the experimental set up in a vacuum chamber, a torque detection of 10 -15 Nm seems within reach. The rotating object could be either a metallic strip in the case of spherical waves like ours, or a plate in the case of traveling waves. This would correspond to EM fields carrying OAM in the microwatt range or lower. These techniques could be an alternative way to detect OAM in the radio domain [29] or even in astronomy [30,31]. Besides, like it has been recently demonstrated for propagating acoustic waves [32], such a detector measures the average angular momentum. Since the power of the beam is known, the topological charge could be then evaluated. Moreover, it could also determine the sign of the OAM.

To conclude, we have demonstrated the transfer of OAM from an electric field to a suspended object using a torsion pendulum. We have used a turnstile antenna to generate a microwave field that carries ℓ = 1 angular momentum in a particular region of space where the contribution is mainly from OAM origin. We have isolated the accelerating regime and we were able to switch the direction of the rotation simply by reversing the phase of the antenna. It would be now stimulating to try to detect microwave field carrying higher order OAM (ℓ > 1). Finally, since the first detection of spin angular momentum transfer was performed on electrons [33],

it may be interesting to reproduce this experiment using electron waves carrying OAM [34,35]. For example, such electron beams would induce a strong controllable magnetization to a nanowire inserted at the center of the vortex electron beam.

We would like to thank K. Wang, T. Teko, and A. Adibi for early interest, and D. Levalois and R. Legave for technical assistance. This work was supported by the University of Rennes 1 via a "défi émergent" action, the French Ministry of Defense (DGA) and the council of Brittany (Région Bretagne). We would also like to thank an anonymous referee for helpful comments.

FIG. 1 .FIG. 3 .

 13 FIG. 1. Experimental set up. G: radio frequency generator, C: -3 dB coupler, A: 40 dB amplifier.

* olivier.emile@univ-rennes1.fr


Michael Grafl

Christian Timmerer

Hermann Hellwagner

George Xilouris
email: xilouris@iit.demokritos.gr

Georgios Gardikis
email: gardikis@iit.demokritos.gr

Daniele Renzi
email: daniele@bsoft.net

Stefano Battista

Eugen Borcoci
email: eugen.borcoci@elcom.pub.ro

Daniel Negru
email: daniel.negru@labri.fr

Scalable Media Coding enabling Content-Aware Networking

Keywords: scalable media coding, content-aware networking, in-network adaptation, content-aware buffering I

Given that multimedia services are becoming increasingly popular, they are expected to play a dominant role for the Future Internet. In this context, it is essential that Content-Aware Networking (CAN) architectures, as envisaged in the frame of the Future Internet, explicitly address the efficient delivery and processing of multimedia content. This article proposes adopting a content-aware approach into the network infrastructure, thus making it capable of identifying, processing, and manipulating (i.e., adapting, caching, etc.) media streams and objects in real time towards Quality of Service/Experience (QoS/QoE) maximization. Our proposal is built upon the exploitation of scalable media coding technologies within such a content-aware networking environment and is discussed based on four representative use cases for media delivery (unicast, multicast, peer-to-peer, and adaptive HTTP streaming) and with respect to a selection of CAN challenges, specifically flow processing, caching/buffering, and QoS/QoE management.

INTRODUCTION

The Future Internet (FI) [START_REF] Pan | A survey of the research on future internet architectures[END_REF] development has raised a rich set of research issues given the huge, global impact of this technology and new societal needs for services. A significant trend is recognized towards an information-centric orientation and, consequently, new challenging concepts are emerging. In particular, significant changes in communications and networking are proposed, including novel basic architectural principles. What are the implications of new networking principles for media streaming? How does the deployment of scalable media formats benefit from these developments? Before we answer these questions, let us briefly revisit the approaches towards the FI and the basics of scalable media formats. The new conceptions are generally divided into revolutionary (i.e., clean-slate) and evolutionary approaches. The revolutionary approaches are often referred to as Information-Centric Networking (ICN), which is used as an umbrella term for related concepts such as Content-Oriented Networking (CON) and Content-Centric Networking (CCN) [START_REF] Jacobson | Networking named content[END_REF] [START_REF] Choi | A survey on contentoriented networking for efficient content delivery[END_REF]. On the other hand, evolutionary (or incremental) approaches, such as Content-Aware Networking (CAN), aim at building upon existing Internet infrastructures. In this article we will explain the role of CAN for multimedia services in more detail. We will present four media streaming use cases which characterize different requirements w.r.t. content-aware processing in the network and highlight the utility of scalable media formats.

Clean-slate ICN approaches, as surveyed in [START_REF] Pan | A survey of the research on future internet architectures[END_REF] and [START_REF] Choi | A survey on contentoriented networking for efficient content delivery[END_REF], are very promising but raise a long list of research challenges like the degree of preservation of the classic transport (TCP/IP) layering principles, naming and addressing, content-based routing and forwarding, management and control framework, in-network caching, energy efficiency, trust, security embedded in the content objects, Quality of Service and Experience, and media flow adaptation. Additionally, new business models are needed for users, content producers, consumers, and service/network providers; deployment issues such as compatibility with existing equipment, scalability, and privacy become crucial.

In parallel, evolutionary approaches towards the FI like Content-Aware Networking are proposed in [START_REF] Koumaras | Media Ecosystems: A Novel Approach for Content-Awareness in Future Networks[END_REF] and developed within the ALICANTE (Media Ecosystem Deployment Through Ubiquitous Content-Aware Network Environments) project [START_REF]ALICANTE Web site[END_REF], enabling efficient routing and forwarding of content based on given content and context characteristics including the adaptation thereof. ALICANTE deploys content-and context-aware strategies at the network edges as discussed in [START_REF] Grafl | Scalable Video Coding in Content-Aware Networks: Research Challenges and Open Issues[END_REF]. A main challenge of evolutionary approaches is obviously to overcome limitations of the current Internet [START_REF] Pan | A survey of the research on future internet architectures[END_REF].

The ALICANTE content-aware network environment attempts at optimizing network resource utilization while maintaining the expected Quality of Service (QoS) and Quality of Experience (QoE) respectively. For this purpose:  It establishes virtual networks on top of the physical infrastructure, which feature inherent content awareness, e.g., by dynamically providing network resources appropriate for different content types.  It provides in-network media caching as well as real-time adaptation, exploiting scalable media coding formats, such as Scalable Video Coding (SVC), which are a vital component towards this objective thanks to their compression efficiency and flexibility [START_REF] Grafl | Scalable Video Coding in Content-Aware Networks: Research Challenges and Open Issues[END_REF].

Both aforementioned functions are provided by enhanced network nodes, the Media-Aware Network Elements (MANEs), which feature virtualization support, contentawareness, and media processing, as well as buffering and caching.

MANEs take advantage of SVC technology in order to achieve in-network media processing. SVC is an extension of MPEG-4 Advanced Video Coding (AVC) and requires a moderate compression overhead of around 10% over single layer coding (i.e., AVC) [START_REF] Wien | Performance Analysis of SVC[END_REF]. In SVC, the video bitstreams are encoded following a layered approach comprising an AVC-compliant base layer providing the basic quality (e.g., temporal, spatial, SNR) and one or more incrementally added enhancement layers. For example, the base layer provides the content quality needed for legacy devices or mobile devices (e.g., 720p) while, with additional enhancement layers, high-definition (e.g., 1080p) and beyond could be reached. Currently, the next generation of SVC is being developed within MPEG based on the High Efficiency Video Coding (HEVC) technology [START_REF]Joint Call for Proposals on Scalable Video Coding Extensions of High Efficiency Video Coding (HEVC)[END_REF].

SVC enhancement layers serve various adaptation purposes in media streaming. As a rule of thumb, spatial SVC enhancement layers support heterogeneous devices with different display resolutions, while SNR (bit-rate) and/or temporal enhancement layers rather enable dynamic adaptation towards available bandwidth.

The aim of this article is to describe the role of scalable media coding formatssuch as SVCin Content-Aware Networks and to propose new solutions for some use cases. Therefore, we will describe a set of use cases (Section II) and provide an analysis thereof regarding a selection of CAN challenges (Section III), specifically flow processing, caching/buffering, and QoS/QoE management. Finally, we provide conclusions in Section IV.

II. USE CASES

In this section we will illustrate use cases highlighting the benefits of using SVC in CAN ranging from unicast and multicast to P2P and adaptive HTTP streaming.

A simplified and generic high-level system overview for the use cases in question is depicted in Figure 1 comprising the following entities: two senders (S1, S2), two MANEs (MANE1, MANE2), and three receivers (R1, R2, R3) with different terminal and (potentially) network capabilities, to which three end-users (U1, U2, U3) are connected. Our discussion of the use cases addresses streaming of non-live content (e.g., Video on Demand), unless noted otherwise. Please note that in more complex scenarios, more senders, even more receivers, and additional MANEs distributed over multiple autonomous network domains may be deployed. These use cases are subsequently analyzed in Section III with respect to content-aware networking aspects.

A. Unicast Streaming

For the unicast use case we have only one sender (e.g., S1), which streams the scalable video content to a single receiver (e.g., R3), like in a traditional Video on Demand application (see Figure 2). This layered media coding approach enables MANEs along the path to perform contentaware operations such as in-network content adaptation. For example, a MANE can react to changing network conditions (based on information provided by a network monitoring system) by dropping enhancement layers of the SVC stream. In current deployments, RTP is typically used as the transport protocol and RTSP is used for session control. Note that in the unicast use case the SVC stream is typically sent via single-session transmission mode over RTP, i.e., all SVC layers are packed into one RTP session.

B. Multicast Streaming

The second use case is multicast streaming, which is characterized by a single sender providing the same content to multiple receivers. This use case is obtained if one sender (e.g., S2 in Figure 1) is streaming the content to heterogeneous trees of MANEs and subsequently to multiple receivers (e.g., R1, R2, R3). The term heterogeneous trees denotes a set of trees, allocated for different SVC layers. All trees have the same root (e.g., S2) but different leaves, depending on the transported SVC layer (e.g., the SVC base layer is delivered to all receivers, while the highest SVC layer is only received by R3), as shown in Figure 3.

Scalable media formats enable the realization of this use case via receiver-driven layered multicast (RDLM) [START_REF] Mccanne | Receiver-driven layered multicast[END_REF] and with SVC this approach is becoming efficient enough to surpass simulcast [START_REF] Grafl | Scalable Video Coding in Content-Aware Networks: Research Challenges and Open Issues[END_REF]. In RDLM, different layers are transmitted over separate multicast groups. RTP realizes this via the multi-session transmission mode, where SVC layers are separated into multiple RTP sessions at the sender side, and re-arranged to the proper SVC bitstream at the receiver side. Each receiver only subscribes to those layers that it supports and that its network link can handle.

Again, a MANE can react to changing network conditions by adjusting the number of layers to which it is subscribed. Such an approach simplifies the adaptation operations. MANEs can transparently neglect the video header information, since the mapping of SVC layers to multicast groups is realized at a lower level, simplifying the process of content adaptation. In other words, a MANE simply adjusts the number of subscribed RTP sessions without having to inspect each and every RTP packet header.

C. Peer-to-Peer Streaming

In a P2P streaming use case, multiple senders exist and every sender provides some parts of the content called chunks or pieces, while one or possibly more receivers consume the content. A scalable media format enables each receiver to request only those layers which are supported by its media player.

In contrast to conventional P2P content distribution, P2P streaming has the timing constraint that every piece must arrive before its playout deadline expires. P2P streaming systems typically use a sliding window of pieces which are currently relevant for the receivers. Within this sliding window, a piece-picking algorithm at the receiver side takes care of downloading those pieces that provide the highest quality to the end-user. The piece-picking algorithm ensures that the base layer is always received before the deadline, determines enhancement layers that can be downloaded under the current network conditions, and takes care of the peer selection for each piece [START_REF] Eberhard | An Evaluation of Piece-Picking Algorithms for Layered Content in Bittorrent-based Peer-to-Peer Systems[END_REF].

While a P2P system is traditionally organized as an overlay network that is transparent to the core network, a content-aware network will allow MANEs to participate in the streaming process in several ways. Figure 4 shows an A MANE can participate in P2P streaming by caching pieces in a content-aware manner or by acting as a peer itself as discussed later in this article (in Section III.A).

D. Adaptive HTTP Streaming

The previous use cases have shown streaming scenarios with various numbers of senders and receivers. In order to overcome common shortcomings of RTP-based streaming such as network address translation (NAT) and firewall issues, this use case introduces adaptive HTTP streaming (e.g., MPEG-DASH) in the context of CAN. In HTTP streaming, the content is typically fragmented into segments which are downloaded by the receiver via individual HTTP (partial) GET requests. This approach allows for a stateless sender and enables at the same time caching at the MANEs and dynamic content adaptation at the client. Based on several industry solutions, MPEG has recently standardized Dynamic Adaptive Streaming over HTTP (MPEG-DASH) [START_REF] Sodagar | The MPEG-DASH Standard for Multimedia Streaming Over the Internet[END_REF].

HTTP streaming is typically used in unicast mode, but multicast or even P2P streaming modes are also possible.

In unicast mode, the sender provides a so-called manifest file of the content, which describes the structure of the media segments and the available media representations. A media representation denotes a particular encoding configuration of the content, e.g., bit-rate or resolution. For layered coding formats such as SVC, those representations can define either the individual layers or even sub-sets of layers of the bitstream. The receiver selects the appropriate representation based on its (rendering) capabilities and starts requesting continuous segments of the content from the sender. MANEs along the network path can act as caches or as content delivery network (CDN) nodes, as shown in Figure 5.

Although HTTP is a unicast protocol, the concept of HTTP streaming can also be applied to multicast streaming. If MANEs along the network path between sender and receivers cache the content segments for subsequent requests by other receivers, the result will be a multicast-like tree. The technical considerations of this approach are discussed later in Section III.B.

The concept of HTTP streaming can even be applied to multisource streaming scenarios similar to P2P streaming. The manifest file can contain multiple sources for each segment including dynamic updates thereof. The receiver may select any of them to download the segments, thus, balancing the load among the senders.

III. ANALYSIS OF USE CASES

We have described different use cases for multimedia streaming and how they can be applied in content-aware networks. In this section we will provide an analysis concerning content-aware network operations, such as flow processing, caching and buffering, and QoS/QoE management for the use cases in question and present some recent scientific advances.

A. Flow Processing

In the unicast use case, the usage of scalable media formats like SVC in a content-aware network brings three main advantages.

First, the sender can easily adapt the content to the receiver's capabilities by only sending those layers that are actually supported by the receiver (e.g., in terms of spatial resolution).

Second, a MANE can perform efficient in-network adaptation of the content in reaction to network fluctuations. That is, when a MANE detects a decrease in available downstream bandwidth that prevents the entire content from being transmitted, it can drop some higher layers of the media stream, assuring continuous playout of at least the base quality at the receiver. Although the end-user receives the content at a lower bit-rate, the actual QoE may increase compared to the alternative which would cause the playout either to stall or to show too many visual artifacts due to high packet loss rate. As soon as the network conditions return to normal, the MANE can re-increase the number of forwarded layers. Each decision about dropping or forwarding SVC layers is triggered by a distributed network monitoring system, which detects network fluctuations and raises appropriate alarms.

The choice which SVC layers to drop or to forward is solved by an adaptation decision-taking engine (ADTE). The ADTE is actually not specific to SVC adaptation but is used for steering any adaptation of contentbe it at the MANE or outside the network at the sender or receiver. Based on context parameters and the description of possible adaptation options, the ADTE runs an optimization algorithm that finds the best-suited choice for the current situation. In the case of in-network SVC adaptation, the set of context parameters is reduced to the network parameters and possible adaptations are limited to SVC layers, making this task rather simple and fast to compute.

Third, a MANE can signal its monitoring information about the network condition upstream to the sender, allowing for sender-side adaptation. While in-network adaptation is a good solution for mitigating short-term network fluctuations, it wastes bandwidth between the sender and the MANE in case of longer periods of decreased available bandwidth. In other words, if a higher layer packet is to be discarded at a MANE anyway, it is useless to transmit it to that MANE in the first place. Note, however, that network-aware adaptation at the sender needs at least one round-trip time (from MANE to sender) to take effect.

In the multicast use case, MANEs can adapt to changing network conditions by subscribing to or unsubscribing from multicast groups containing SVC enhancement layers. Conventional layered multicast is receiver-driven [START_REF] Mccanne | Receiver-driven layered multicast[END_REF], i.e., the receivers control the subscriptions to multicast groups. Hence, in-network adaptation is achieved implicitly as the receiver controls it through subscription to appropriate SVC layers. MANEs aggregate and combine subscriptions from downstream entitiesboth receivers and MANEsusing them for subscribing to appropriate SVC layers upstream. ALICANTE adopts and extends the RDLM approach for the distribution of video content in multicast-based scenarios.

There are two possibilities for MANEs to assist the network-aware adaptation of multicast streaming. Either, downstream forwarding of one or more SVC layers can be temporarily truncated in case of congestion at an outgoing link as discussed in [START_REF] Zhang | Router-assisted layered multicast[END_REF], or a MANE can control multicast group subscriptions by sending prune or graft messages to upstream neighbors as defined in RFC 3973 [START_REF]Protocol Independent Multicast -Dense Mode (PIM-DM): Protocol Specification (Revised)[END_REF].

MANEs can also improve multicast functionalities of existing network infrastructures. If native multicast is not supported, MANEs may perform overlay multicast with adjacent MANEs, so that they become bridges between native and overlay multicast, as it is done in ALICANTE [START_REF]ALICANTE Web site[END_REF]. Furthermore, ALICANTE supports traffic engineering as well as content and service classification and differentiation mechanisms (i.e., DiffServ and MPLS) that enable selective treatment of SVC layers, e.g., increasing priority and robustness of the base layer.

For the P2P streaming use case, a MANE may act as a peer, autonomously requesting pieces which it deems relevant for any connected receivers. Running a P2P engine on a MANE increases the processing requirements for this entity but it also offers a flexible and powerful way to participate in P2P streaming. The MANEs thus form a P2P overlay network (at the CAN layer) that may closely cooperate with the overlay network at the application layer. The aforementioned flow processing policies are also applicable to adaptive HTTP streaming with some noticeable differences. TCP uses reliable transmission which is not suitable for in-network adaptation achieved through enhancement layer dropping. If a MANE simply drops TCP packets of an enhancement layer to avoid network congestion, it would trigger the sender to retransmit the packets after TCP timeout. For the streaming session, the retransmission of the packet wastes bandwidth and even if the packet reached the receiver eventually, it would probably arrive after the playout deadline. Thus, for HTTP streaming a MANE shall act as a (transparent) proxy cache in combination with CDN functionality as will be described in the following section. As the adaptation logic is entirely located at the receiver side, in-network adaptation is achieved implicitlysimilar to the multicast use caseby means of HTTP requests for layers that are supported by the receiver. Requests for individual SVC layers can be answered by different network nodes (or by the sender), depending on where these layers are buffered. Hence, adaptation occurs within the network, but without active participation by the MANEs.

The aforementioned adaptation mechanismsimplicit or explicitprovide a powerful tool for mitigating the effects of network fluctuations. Furthermore, the adaptation decision-taking (i.e., the selection of which SVC layers to forward) has to be performed in a distributed manner. That is, each MANE computes its local adaptation decision and coordinates it with the other nodes in the network. Efficient, scalable signaling and coordination of adaptation decisions is still an open research challenge [START_REF] Grafl | Scalable Video Coding in Content-Aware Networks: Research Challenges and Open Issues[END_REF].

B. Caching and Buffering

MANEs can buffer previously requested content and may even act as CDN caches, i.e., proactively moving the content closer to the receivers. Note that the storage requirements for CDN-enabled MANEs are considerably higher than for mere buffering support.

In the unicast use case, a CDN-enabled MANE can proactively perform caching of popular content. In particular, prefix caching decreases start-up delay while also reducing network traffic. When a receiver requests the content, the MANE starts streaming from its cache while requesting the suffix of the content from the sender [START_REF] Kayssi | RTP-based caching of streaming media[END_REF].

The usage of SVC offers a tradeoff between quality and availability to the MANE. The prefix cache may contain only the base layer for less popular content. Thus, the enduser starts receiving only the base layer, but with a low startup delay, and later the enhancement layers from the sender are added.

Proactive caching can also be used in the multicast use case to reduce mainly start-up delay but also network traffic. Note that proactive caching is not applicable to live streaming sessions. Moreover, all receivers are served simultaneously via multicast RTP streams, abolishing the need for buffering at MANEs.

In the P2P streaming use case, a MANE can aggregate requests for a piece and buffer downloaded pieces for subsequent requests. Especially in live scenarios, almost all receivers share the same time window for the content; thus, each piece will be highly popular for a short time span. By buffering a piece during this time frame, the MANE will be able to reduce network utilization and latency even with a limited buffer size. In most cases, such behavior is transparent to the peers within the traditional, application layer P2P overlay network.

Additionally, the MANE may also aggregate requests for the same piece to different senders and only forward one request which we call content-aware buffering. Unlike conventional, the MANE may intercept requests and transmit a buffered piece instead of forwarding them. This approach would constitute an evolutionary implementation of the CCN functionality [START_REF] Jacobson | Networking named content[END_REF]. However, a small drawback of this approach is that the peer selection of the first receiver might not always be the optimal selection. But once the MANE has downloaded and buffered the entire piece, the issue is alleviated.

A MANE might also act as a peer, proactively requesting pieces that may be needed in the near future by any receivers connected to it. Thus, the MANE increases the replication of the content and moves it closer to the receivers. However, this puts some additional performance and storage requirements on the MANE.

Caching and buffering are also integral parts of the adaptive HTTP streaming use case. In unicast mode, a MANE can provide CDN functionalities similar to the unicast use case discussed above. In contrast to RTP-based streaming, HTTP streaming immediately benefits from existing HTTP caching [START_REF] Sánchez De La Fuente | iDASH: improved dynamic adaptive streaming over HTTP using scalable video coding[END_REF] that may be deployed on top of content-aware networks. The multicast mode relies on buffering and request aggregation at the MANE for bandwidth-efficient streaming. As mentioned before, intelligent buffering at MANEs along the network path between sender and receivers constructs a bandwidthefficient multicast tree. In order for the buffer size at the MANE to remain inside a reasonable limit, two requirements must be met. On the one hand, all receivers must share the same time window so that the popularity of a segment is temporarily limited. This time window can be signaled in the manifest file, as it is typically the case for live streaming services [START_REF] Sodagar | The MPEG-DASH Standard for Multimedia Streaming Over the Internet[END_REF]. On the other hand, the MANE has to be aware of the streaming session in order to buffer the segments accordingly. The straightforward solution is for the MANE to parse the manifest file and to retrieve such information from there. An alternative solution would be that the MANE learns about the best buffering policy from a statistical analysis of the stream.

In the multisource mode of HTTP streaming, buffering at MANEs has similar effects as in P2P streaming. That is, MANEs aggregate requests (even to different senders) and perform content-aware buffering of downloaded segments for the duration of the sliding window of the streaming session. An open research challenge is the impact of the discussed request aggregation on the load balancing strategies between the senders.

In a recent study, Lederer et al. have proposed a peerassisted HTTP streaming architecture compliant with MPEG-DASH [START_REF] Lederer | Towards peer-assisted dynamic adaptive streaming over HTTP[END_REF]. For each segment, the server lists a selection of possible peers in the manifest file. Those peers have already downloaded the segment and provide it through local HTTP servers. Other clients download segments from those peers if their buffer fill level guarantees smooth playback. Even under the consideration that clients have asymmetric Internet connections with significantly lower uplink bandwidth than downlink bandwidth, the solution reduces server bandwidth by up to 25%. While that work [START_REF] Lederer | Towards peer-assisted dynamic adaptive streaming over HTTP[END_REF] focuses on conventional client peers, MANEs can act as peers just as well. Since MANEs are usually not limited by asymmetric connection speeds, server bandwidth can be further reduced. To validate this assumption we performed simulations with the same setup as [START_REF] Lederer | Towards peer-assisted dynamic adaptive streaming over HTTP[END_REF], except that MANEs acting as peers had symmetric connection speeds (15 peers with 16 Mbps and 25 peers with 8 Mbps). Like in the original evaluation, the maximum bitrate of the content was set to 1,400 kbps. The simulation results of server bandwidth requirements over time are shown in Figure 6. Original server bandwidth for asymmetric connection speeds of peers is labeled Peer Assisted, server bandwidth for symmetric connection speeds is labeled Peer Assisted (MANE). MANEs acting as peers in this HTTP streaming scenario were able to reduce server bandwidth by up to 29.5%. It should be noted that the simulation did not consider frequent updates of the manifest file, which contains the current list of peers. Updating the manifest file every 60 or 120 seconds would bring further performance gains.

The deployment of SVC in HTTP streaming also brings benefits to caching and buffering mechanisms. While HTTP streaming of non-layered media formats requires switching between different content representations (e.g., frame-rate, resolution, quality) for adaptation, SVC-based adaptation is performed by adding/removing enhancement layers. Thus, the MANE only has to cache one SVC stream instead of multiple streams for different representations. This both reduces storage requirements and increases cache performance. Simulations conducted by Sánchez et al. compared the combination of SVC-based HTTP streaming and a streaming-optimized caching strategy to AVC-based streaming under Least Recently Used (LRU) strategy [START_REF] Sánchez De La Fuente | iDASH: improved dynamic adaptive streaming over HTTP using scalable video coding[END_REF]. Their results show that the cache hit ratio can be increased by up to 11.5 percentage points for congestion in the cache feeder link (i.e., the link between the sender and the cache) and by up to 25.7 percentage points for congestion in the access links.

C. QoS/QoE Management

A primary goal of content-aware networking is to manage and optimize the QoS and consequently QoE at the application level.

The term QoS describes properties of the network that influence the transport of media flows. Metrics like delay, packet loss, and jitter are used to measure QoS. The more recently coined term QoE targets the degree of delight or annoyance of the user about an application or service. Besides QoS parameters, also user-related factors (e.g., expectations) as well as terminal capability and performance play a role in QoE. QoE is typically measured as Mean Opinion Score (MOS) based on user ratings. More information on QoS and QoE can be found in [START_REF]Qualinet White Paper on Definitions of Quality of Experience[END_REF].

QoS/QoE optimization can be achieved through contextaware mechanisms both at the end-user side and within the (core) network. At the end-user side, several aspects of the usage environment (such as terminal capabilities) can be taken into account during content request and consumption.

Other aspects such as user preferences and the current status of the end-user terminal may even dynamically affect the configuration of the requested SVC stream.

Within the (core) network, context-awareness relates to the current condition of the network. Network monitoring enables MANEs to react to network fluctuations by performing in-network adaptation of SVC content. Monitoring information is used locally and is aggregated at the CAN level for managing the network behavior and establishing long-term adaptation policies [START_REF] Koumaras | Media Ecosystems: A Novel Approach for Content-Awareness in Future Networks[END_REF].

One important aspect is the appropriate media encoding configuration. The ALICANTE project is working on encoding guidelines for SVC that facilitate distributed adaptation. Those guidelines will comprise a description of typical resolutions, which and how many bitrates to use for each resolution, appropriate scalability modes (temporal, spatial, or SNR), how to combine these modes, differences among use cases, and more. On the other end of the media delivery chain, the project investigates the video quality at the client when there have been packet losses in any of the SVC layers. Evaluations are performed using a no-reference QoE tool called ALICANTE Pseudo Subjective Quality Assessment (A_PSQA) [START_REF] Cherif | A_PSQA: PESQ-like non-intrusive tool for QoE prediction in VoIP services[END_REF], which uses a continuous QoE score ranging from 1 (excellent) to 0 (bad) to estimate video quality based on packet loss characteristics. The SVC streams used in the experimental setup comprised three layers. Figure 7 shows how the quality of a video degrades for packet loss at any of these layers.

The QoE scores are subsequently used for triggering the adaptation and enhancing the granularity by which the system reacts to context variations. Thus, QoE evaluations are a vital part of advanced adaptive media delivery systems.

As already mentioned, SVC enables a fine-grained control over the QoE at the network level. A non-scalable media format will suffer from severe QoE degradation if not all packets of the stream are transmitted. With SVC, lower layers can be prioritized, maintaining smooth and undistorted playout with controlled QoE degradation. SVC can also be conveniently combined with error recovery techniques at the decoding side, in order to further enhance the QoE perceived by the user.

As a conclusion, Table 1 summarizes the discussed CAN-related challenges for each of the described use cases. Note that for QoS/QoE management we make no explicit distinction between the use cases.

IV. CONCLUSIONS

Scalable media coding formats (such as SVC) in combination with in-network adaptationand, as a consequence, its capabilities in terms of flow processing, caching/buffering, and QoS/QoEare becoming promising concepts towards enabling content-awareness within the (core) network. This concept is referred to as Content-Aware Networking (CAN) and provides an elegant, powerful, and flexible tool to accommodate existing and imminent challenges for a variety of traditional and emerging use case scenarios in the context of multimedia delivery within the Future Internet. We have argued that sender-driven use cases such as unicast and multicast streaming greatly benefit from contentawareness for routing and forwarding. In P2P streaming, the combination of enhanced forwarding and buffering techniques may allow MANEs to collaborate with receivers within the P2P network. Content-and context-aware caching/buffering are furthermore important aspects in the adaptive HTTP streaming use case.

Interesting challenges remain, such as the integration of on-the-fly QoE evaluation of SVC content for adaptive media streaming or the further improvements to the involvement of MANEs into P2P streaming. As future trends indicate more advanced video compression technologies targeting resolutions beyond 1080p, (e.g., a new scalable extension for HEVC), efficient and reliable buffering at MANEs becomes increasingly important in order to reduce overall network loads. Furthermore, adaptive HTTP streaming becomes increasingly popular due to its relatively easy deployment. Therefore, our future work will focus on how MANEs can further improve the existing HTTP infrastructure.

Figure 1 .

 1 Figure 1. High-Level System Overview.

Figure 2 .

 2 Figure 2. Unicast Streaming in Content-Aware Networks.

Figure 3 .

 3 Figure 3. Multicast Streaming in Content-Aware Networks.

Figure 4 .

 4 Figure 4. P2P Streaming in Content-Aware Networks.

Figure 5 .

 5 Figure 5. Adaptive HTTP Streaming in Content-Aware Networks.

Figure 6 .

 6 Figure 6. Simulation of peer-assisted HTTP streaming with MANEs as peers.Time [sec] Utilized Server Bandwidth[Mbps]

Figure 7 .

 7 Figure 7. QoE scores vs. (a) loss rate at SVC base layer and enhancement layer 1, and (b) loss rate at enhancement layer 1 and enhancement layer 2 with base layer loss rate of 10%.

Table 1 .

 1 Summary of CAN-related challenges addressed by the presented use cases.

			CAN challenge
	Use case	Flow processing	Caching & buffering	QoS/QoE
				management
	Unicast	 explicit adaptation  signaling adaptation decision to sender	 SVC-based prefix caching for low start-up delay	 local & aggregated monitoring of network conditions
	Multicast	 implicit adaptation  multicast bridges for hybrid multicast  differentiated forwarding of	 SVC-based prefix caching for low start-up delay  no buffering	 smooth, undistorted playout via SVC
		SVC layers
	P2P streaming	 explicit adaptation  peer for CAN P2P overlay	 aggregating requests  content-aware buffering of pieces
		network	within sliding window
	Adaptive HTTP streaming	 implicit adaptation  transparent proxy cache	 SVC-based prefix caching  multicast: buffering within sliding window
			 multisource streaming: aggregating
			requests & content-aware buffering
			within sliding window

ACKNOWLEDGMENT

This work was supported in part by the EC in the context of the ALICANTE project (FP7-ICT-248652). The authors thank Stefan Lederer for his help with simulation results.


François Portet
email: fportet@abdn.ac.uk

P Wave Detector with PP Rhythm Tracking: Evaluation in Different Arrhythmia Contexts

Keywords: P-wave detection, ECG, knowledge based pattern recognition, cardiac monitoring

Automatic detection of atrial activity (P waves) in electrocardiogram (ECG) is a crucial task to diagnose the presence of arrhythmias. P wave is difficult to detect and most of the approaches in the literature have been evaluated on normal sinus rhythms and rarely considered arrhythmia contexts other than atrial flutter and fibrillation. A novel knowledge-based P wave detector algorithm is presented. It is self adaptive to the patient and able to deal with certain arrhythmias by tracking the PP rhythm. The detector has been tested on 12 records of the MIT-BIH arrhythmia database containing several ventricular and supra-ventricular arrhythmias. On the overall records, the detector demonstrates Se=96.60% and Pr=95.46%; for the normal sinus rhythm, it reaches Se=97.76% and Pr=96.80% and, in the case of Mobitz type II, it demonstrates Se=72.79% and Pr=99.51%. It also shows good performance for trigeminy, and bigeminy and outperforms some more sophisticated techniques. Although the results emphasize the difficulty of P wave detection in difficult arrhythmias (supra and ventricular tachycardias), it shows that domain knowledge can support efficiently signal processing techniques.

Introduction

Automatic electrocardiogram (ECG) analysis is a fundamental task in cardiac monitoring and Holter analysis to detect cardiac arrhythmias. The ECG reflects two main heart activities: the ventricular activity, represented by the QRS complexes and the T waves; and the atrial activity, represented by the P waves. The detection of the atrial activity is a crucial task to diagnose the presence of supra-ventricular arrhythmias as well as to confirm the presence of ventricular arrhythmias [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF]. If the ventricular activity has been studied for many decades and several efficient solutions have been proposed [START_REF] Kohler | The principles of software QRS detection[END_REF]Portet et al.,2005;de Chazal and Reilly,2006), the detection of the atrial activity is still an unsolved problem, especially in arrhythmic situations. P waves are difficult to detect particularly for four main reasons [START_REF] Hernández | Improvement of a P-wave detector by a bivariate classification stage[END_REF]:

1. P-waves have low signal-to-noise ratio (SNR), especially in clinical ECG and Holter; 2. P waveforms show high inter-patient variability, which makes a non adaptive approach inadequate; 3. P waves have no exclusive time and frequency characteristics, which makes a source separation approach difficult; 4. P waves may be invisible on the ECG signal, in case of atrioventricular (AV) dissociations. The AV dissociation leads to an ECG where QRS, T and P wave occurrences do not respect the normal time ordered sequence (P, QRS, T), and the P wave may be hidden by a bigger wave (QRS or T).

Due to the lack of annotated beat-to-beat P-wave databases, several P wave detection approaches have not been evaluated quantitatively. Moreover, some approaches, use a learning method that needs a pre-identification of the P waves, which might be too time consuming for the clinician in the stressful environment of Intensive Care Unit (ICU). Finally, some of the approaches are so complex that they are very hard to reproduce.

In this paper, we present a P wave detector algorithm that uses signal processing methods supported by knowledge about the cardiac domain. This algorithm is real-time implementable and tackles four main difficulties:

(1) The low SNR of the P wave is enhanced by an input filter that removes baseline wandering and highfrequency noise. Then, the algorithm localizes the area in which a P wave is most likely to appear and focuses the P wave detection solely in this area.

(2) The P wave variability is tackled by adapting the detection to the most recently detected P waves. Thus the algorithm adapts itself to the patient's P waves.

(3) The detection of P waves is based on generic heuristics that do not depend on pre-learning the patient characteristics, as is often required in the literature [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF][START_REF] Madhukar | Parametric technique for P wave delineation[END_REF]Pilla and Lopes,1999).

(4) Some of the AV dissociations are solved by tracking the PP rhythm (temporal distance between two successive P waves). We do not focus the detection on atrial flutter and fibrillation which are particular processes and for which promising studies have been undertaken (Dotsinsky,2007;Rieta and Hornero,2007;[START_REF] Stridh | Sequential Characterization of Atrial Tachyarrhythmias Based on ECG Time-Frequency Analysis[END_REF][START_REF] Vayá | Convolutive Blind Source Separation Algorithms Applied to the Electrocardiogram of Atrial Fibrillation: Study of Performance[END_REF].

We do not deal with invisible P waves (hidden by the QRS or T waves) in our method. Although hidden P waves can be crucial for identification of some cases of AV dissociations, the absence of P wave is informative in many other rhythms. For example, the absence of a P wave before a QRS (either because the QRS hides the P wave or because no P wave exists) re-enforce the suspicion that the heart beat is abnormal.

We have compared our detector to another algorithm described in the literature [START_REF] Laguna | Automatic Detection of Wave Boundaries in Multilead ECG Signals: Validation with the CSE Database[END_REF] and both have been evaluated on a large database (Moody and Mark,2001) that contains ventricular and supra-ventricular arrhythmias as well as normal sinus rhythms. To our knowledge, several studies are focused on P wave detection in normal sinus rhythm ECGs only. Except for AFL and AF (Dotsinsky,2007;Rieta and Hornero,2007), few studies have been undertaken also in presence of arrhythmias.

The following section gives a short review of the state of the art. Then, the implemented P wave detector is described in section III. The dataset used for evaluation is presented in section IV. The evaluation of the P wave detection, for which there are no standard procedures as yet, is explained in section V. Then, the results of our approach are compared to another detector from the literature in Section VI. These results are analyzed according to the context of the cardiac rhythm in order to assess the performance of the detectors in a more accurate way than common evaluations found in the literature. Finally, the approach and the results are discussed in section VII and conclusions are presented in section VIII.

Review of P wave detection algorithms

The algorithms found in the literature are mainly based on three approaches: a) localized search area methods, b) ventricular and atrial source separation methods, and c) Esophageal electrocardiogram (EECG) based methods.

Localized search area

The first method consists in searching for the P wave in a localized area outside the QRS-T (generally before the QRS) (Agilent Technologies,2000;[START_REF] Almeida | Automatic delineation of T and P waves using a wavelet-based multiscale approach[END_REF][START_REF] Goutas | Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation[END_REF]. Once the area is defined, the P wave can be detected directly by: heuristics (Agilent Technologies,2000); digital fractional differentiation [START_REF] Goutas | Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation[END_REF]; differentiated low-pass filtering [START_REF] Laguna | Automatic Detection of Wave Boundaries in Multilead ECG Signals: Validation with the CSE Database[END_REF]; wavelet analysis [START_REF] Almeida | Automatic delineation of T and P waves using a wavelet-based multiscale approach[END_REF][START_REF] Martinez | A wavelet-based ECG delineator: evaluation on standard databases[END_REF][START_REF] Sovilj | Real time P-wave detector based on wavelet analysis[END_REF]; wavelet transform and neural network (Sternickel,2002); morphological transform [START_REF] Sun | Characteristic wave detection in ECG signal using morphological transform[END_REF]; template matching (Dotsinsky,2007); or combination of Hidden Markov Model and wavelet (Graja and Boucher,2005). The localized search area approach presents satisfactory results for ECGs where the QRS is easy to identify. However, it relies only on ventricular activity (QRS) to estimate atrial activity (P wave); thus, it is not able to deal with atrioventricular (AV) dissociations. Nevertheless, a recent approach (Dotsinsky,2007), that is close to the one presented in this paper, showed that, using adaptive threshold and templates similar to P-wave templates, a good detection of AFL and AF can be reached with the detection of visible F-or f-waves.

Ventricular and atrial source separation

The second approach consists in viewing P wave detection as a source separation problem. It first separates atrial activity from ventricular activity, either by direct separation or by QRS-T cancellation. This leads to a signal with an increased SNR in which the P waves are detected. This approach is expected to perform better in the presence of AV dissociations. The most common approach is the subtraction of a QRST template from the original signal [START_REF] Gritzali | Detection of the P and T waves in an ECG[END_REF][START_REF] Hernández | Improvement of a P-wave detector by a bivariate classification stage[END_REF][START_REF] Lemay | Spatiotemporal QRST cancellation method using separate QRS and T-waves templates[END_REF]Thakor and Zhu,1991). To prevent poor performance in case of variation of the beat morphology, others methods, compared in (Rieta and Hornero,2007;[START_REF] Vayá | Convolutive Blind Source Separation Algorithms Applied to the Electrocardiogram of Atrial Fibrillation: Study of Performance[END_REF] have been proposed consisting in neural networks [START_REF] Vasquez | Atrial activity enhancement by Wiener filtering using an artificial neural network[END_REF], blind source separation or independent component analysis. After the cancellation, the atrial activity may be analyzed by a classification stage [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF][START_REF] Hernández | Improvement of a P-wave detector by a bivariate classification stage[END_REF], or, more recently, by spectral profile analysis [START_REF] Stridh | Sequential Characterization of Atrial Tachyarrhythmias Based on ECG Time-Frequency Analysis[END_REF].

Although these approaches separate ventricular activity from atrial activity, they remain sensitive to the presence of abnormal beats. Moreover, the QRS occurrence is not taken into account in detecting the P wave.

Although the QRS is not useful in the presence of AV dissociations, it could be exploited in other situations to support P wave activity detection.

Esophageal electrocardiogram (EECG) based

The EECG is a semi-invasive technique used to obtain an amplified representation of the atrial activity (atria are close to the oesophagus). Despite its good SNR enhancement and the improvement of P wave detection [START_REF] Hernández | Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring[END_REF][START_REF] Jeras | Real Time P-wave Detection in Esophageal ECG[END_REF], this technique is rarely used due to its semi-invasive aspect.

The P wave detector

The proposed P wave detector relies on a QRS detection stage coupled with a PP rhythm estimation to support P wave detection in case of AV dissociations. The proposed method is a knowledge-based approach coupled with a signal processing one. All the parameters used in the study are derived from knowledge of cardiac physiology.

Cardiac physiology is out with the scope of the paper, but all the parameter values have been chosen in accordance with ECG standards (Goldman,1973) and these choices are explained in this paper as much as possible. The whole processing chain, presented in Figure 1, is composed of 5 stages: (1) filtering, (2) QRS detection, (3) P wave occurrence estimation, (4) area selection, and finally (5) P wave detection. Filtering removes the main noise, and then QRS detection and P wave occurrence estimation trigger the area selection process. When triggered, area selection selects the ECG segment to be analyzed by the P wave detection stage. At the initialisation, the P wave buffer is empty so that the P detection relies only on the QRS detector. After a while there are sufficient P waves in the buffer to estimate the next P wave occurrence. Old P waves of the buffer are removed during the process so that the estimation relies only on the most recent previously detected P waves. All the stages are detailed in the following sections.

---INSERT FIGURE 1 ---

Filtering

The filtering stage eliminates baseline wandering by using two consecutive median filters [START_REF] De Chazal | Automatic classification of heartbeats using ECG morphology and heartbeat interval features[END_REF]. The first median filter of width 200ms removes the QRS and P waves. The output feeds a second median filter of 600ms-width to remove the T waves. The resulting signal represents the baseline and is subtracted from the original ECG. The high frequency noise is then removed by a 5-order low pass Butterworth filter with a cutting frequency of 40Hz. After filtering in the forward direction, the filtered signal is then run back through the filter in the reverse order. This results in a filtered ECG with zero phase distortion, and magnitude modified by the square of the original magnitude response.

QRS detection

This study is focused on the P wave detection and does not investigate the QRS detection problem. The QRS occurrence times used in the study are taken from human annotations. However, any QRS detection algorithm or composition of QRS algorithms (Portet and Carrault,2005) can be used at this stage.

P wave occurrence estimation

This stage estimates the next P wave occurrence according to the previously detected P waves. This estimation is then used by the area selection to define in which area of the ECG a P wave should be searched for. The estimation of the next P wave occurrence, called t Pestim , is based on a time ordered buffer of Size N of the previously detected P waves. The PP intervals (temporal distance between two successive P waves) are first computed according to the following expression:

) 1 ( ) ( ) ( }, , , , , 2 { - - = ∈ ∀ i t i t i PP N k i P P Κ Κ (1)
where t P (i) is the time occurrence of the i th P wave in the buffer. The set of indexes

} ] 1 , 6 . 0 [ ) ( : } 1 , , 1 { { s i PP N i I PP ∈ - ∈ = Κ
is then computed. I PP represents the indexes of the PP values that belong to the interval [0.6, 1]s. This range corresponds to [60,100]bpm (beat-per-minute) which are the standard limits for bradycardia and tachycardia in adults (Goldman,1973). This can be justified by the fact that bradycardia is a slow rhythm for which P wave search can be based on the QRS, while tachycardia is a rapid rhythm in which the P wave is seldom visible. In the other cases, these values let the algorithm check whether a P wave is missing between two successive P waves, or two P waves are too close to each other. The estimates of the next P wave occurrence candidates are noted ) ( ˆi t P and are computed independently according to each acceptable P wave in the buffer by:

      - + = > × + = ∈ ∈ ∀ ) ( ) (

Ν*

(2) where t is the current time (absolute time since last QRS detection or P wave detection). Thus, ) ( ˆi t P is computed by adding n times PP(i) to t P (i) until ) ( ˆi t P > t. From all the independent estimations ) ( ˆi t P , the final estimated P wave occurrence t Pestim is computed by:

   ≥ ∈ ∀ = otherwise N I if i t median I i t PP P PP Pestim , ) 2 / ( ),...]), ( ([..., , φ (3)
If there are at least N/2 candidates P t ˆ(i) , then the median of all the P t ˆ(i) is taken to reduce the effect of outliers. As far as we are aware, the benefit of using PP rhythm tracking for the detection of P waves has rarely been investigated [START_REF] Sovilj | Real time P-wave detector based on wavelet analysis[END_REF]). An empirical study analyzing the detection sensitivity according to the buffer length (on records independent from the ones used in the evaluation section with sampling frequency of 360Hz) concluded that an optimal value of the buffer length was 15s.

Area selection

The area selection extracts an ECG segment within which a P wave occurrence will be searched for. An average P wave width W=100ms (the average width of physiological and pathological P waves [START_REF] Guray | Evaluation of P wave duration and P wave dispersion in adult patients with secundum atrial septal defect during normal sinus rhythm[END_REF]), is defined to perform this operation. Area selection is activated if there is a QRS occurrence, or a P wave estimation not followed by a QRS occurrence during a defined period. The process is shown in Figure 2.

---INSERT FIGURE 2 ---Two cases are considered:

1) If the area selection is triggered by the P wave estimation, then the area is estimated by the following expression:

[ ]

     + > + - = + + - otherwise B t t next if B t B t AREA P Pestim QRS P Pestim P Pestim , , , φ (4)
where t Pestim is the estimated P wave occurrence and B - p and B + p are the backward and the forward P boundaries, respectively. The aim of the waiting condition is to sustain P wave detection with a QRS occurrence which is generally more reliable than P wave estimation. P wave estimation is thus used only when the PR interval is long, which can be the case in presence of an AV conduction anomaly. An empirical study performed on independent records (ECG records not used in the evaluation) resulted in the following optimal values: B - p =0.1s and B + p = 0.4s .

2) If the area selection is triggered by the QRS detection, then the area is estimated by equation ( 5) C are the backward and the forward QRS coefficient boundaries, respectively. If the RR interval is larger than RR Limit then the ECG segment is selected. RR Limit is set to 450 ms; this represents the sum of the width of the P wave (100ms) plus the narrowest standard QT interval of 350ms (Goldman,1973). Indeed, a P wave is worth searching for if there is enough time for the P-QRS-T waves to occur. The selection of an area before a QRS is related to the PR interval which varies with the RR interval: the higher the Heart Rate (HR), the shorter the PR interval. A standard PR interval is about 200ms for an 80bpm HR (Goldman,1973). Starting from this knowledge -QRS C and + QRS C have been set to 0.3s and 0.1s; this leads to an area between [-0.3, -0.1]s before the QRS for a 60bpm HR, and [-0.18, -0.06]s before the QRS for a 100bpm HR, which is in accordance with standard values (Goldman,1973). Thus, the system adapts itself to the patient's heart rate and avoids searching for a P wave when a very premature ventricular contraction occurs.

[ ] 1 , , , - + - - =      > × + × - = QRS QRS Limit QRS QRS QRS QRS t t

P wave detection

The P wave detection phase consists in searching for P wave candidates in the area defined by the area selection module. It detects local maxima above a baseline and selects which of the maxima is the most likely to be a P wave. The process is shown in figure 3.

---INSERT FIGURE 3 ---The segment from the area selection is smoothed with a median filter of 36ms-width to reduce artifact. Then, a baseline is estimated by applying a W-width median filter to the absolute of the filtered ECG segment. Every interval of the absolute filtered ECG above the baseline is a possible candidate. The candidates are then eliminated using the tests below:

1-Every interval that starts or ends at the bounds of the area is removed (T end and QRS start) 2-Every interval whose maximum magnitude is outside the interval [MaxMed(P)/3, MaxMed(P)×3], is removed. MaxMed(P) is the median of the P wave maxima magnitudes of the buffer.

After this stage, the remaining intervals (if any) are supposed to contain a P wave. The probability of being a P wave is computed using the magnitude and the width according to formula 6. The most probable P wave is then retained and stored in the P wave buffer.

  

     × = )) ( max( )) ( ( )) ( max( )) ( ( )) ( ( P width i P width P mag i P mag i Pwave P (6)
such windows is a TP. If several detected P waves fall into the same window, only one is counted as TP: the others are ignored. Every detected P wave that does not fall into a window is an FP. Finally, every window that does not contain a detected P wave is an FN. As atrial flutter and atrial fibrillation are particular processes, the episodes corresponding to these arrhythmias are excluded from the evaluation (likewise ventricular fibrillation periods for QRS detector assessment in the ANSI/AAMI standard(ANSI/AAMI standard EC57:1998,1998)). TP, FP, and FN are used to compute three common criteria: Sensitivity (Se) = TP/(TP+FN), Precision (also called Positive Predictivity) (Pr) = TP/(TP+FP), Error Rate (ER) =(FP+FN)/(TP+FN+FP). We also compute the F-Measure (FM) (van Rijsbergen,1979) which combines Se and Pr in a single efficiency measure; FM = 2×Se×Pr/(Se+Pr).

Experiments

Two experiments have been undertaken to assess our P wave detector. Firstly, it has first been compared against the ECG beat segmentation algorithm called ecgpuwave of Laguna et al. [START_REF] Laguna | Automatic Detection of Wave Boundaries in Multilead ECG Signals: Validation with the CSE Database[END_REF] which detects P, QRS and T waves. This algorithm has been validated on several databases and appears to perform heartbeat segmentation with accuracy comparable with inter-expert annotation variation. In our study, ecgpuwave has been fed with the manual QRS annotations to evaluate it in the same conditions as our detector. For a better analysis, the results are presented according to the records (classic presentation) and according to the arrhythmias (new presentation). Finally, to investigate the value of using other kinds of information provided by the QRS analysis, tests with QRS classification and on-line QRS detection have been made and are briefly described at the end of this section.

Global results

Detection results obtained on the selected records are presented in Table 1.

---INSERT TABLE 1 ---

They show that our method presents similar results as ecgpuwave for normal sinus rhythm records (FM=99.70% against FM=99.76%). But our approach outperforms ecgpuwave on the entire dataset with Se=96.60% Pr=95.46% against Se=93.15% and Pr=92.91%. This suggests that our method performs better in the presence of rhythm disorders. However, Table 1 (classical presentation) is not informative enough to emphasize clearly the performance of the detectors in arrhythmic situations where a P wave detector is challenged. To analyze the results in a more informative way, we also present the results relatively to each arrhythmia.

Results according to arrhythmias

Table 2 presents the same information as in table 1, but rearranges the results according to the type of rhythm.

In this table, each row is related to the normal sinus rhythm or to an arrhythmia. For each rhythm, the duration (in seconds) and the number of P wave annotations are given.

---INSERT TABLE 2 ---

According to Table 2, and contrary to the results in table 1, our detector actually outperforms ecgpuwave in normal sinus rhythm episodes. This can be explained by the fact that normal sinus rhythm periods present in the arrhythmic records (106,119,207,214,222,223,231) are also taken into account. These specific periods are surrounded by arrhythmia periods, and also present some bundle branch block anomaly periods that perturb the detection. Thus, this better presentation of the results emphasizes that our detector is actually more robust for the detection of P waves in normal sinus rhythm than ecgpuwave. Figure 4 compares the outputs of ecgpuwave (part a) and the outputs of our method (part b) on an ECG excerpt containing LBBB QRS shapes and premature ventricular contractions.

The rest of the results show that our detector is the best for all the arrhythmias, except for NOD, VT and IVR.

In theses cases both detectors perform quite bad. Satisfactory results are obtained for our detector for some ventricular arrhythmias: B (FM=91.08%), T (FM=93.98%); and some supra-ventricular arrhythmias: BII (FM=84.08%) and AB (FM=79.0.3%). The results obtained for Mobitz type II (BII) demonstrate the ability of our detector to deal with AV dissociation, whereas ecgpuwave missed one out of two, on average (Se=49.76%).

Figure 4 (c andd) shows detection results in a Mobitz II context. ecgpuwave is perturbed, whereas our method detected all the P waves in this situation. Very poor results are obtained for NOD (FM=25.38%) VT (FM=6.89%), IVR (FM=5.07%), and SVTA (FM=33.33%). Figure 4 e) and 4 f) show a SVTA followed by a NOD rhythm in which both methods failed. These rhythms are very difficult to analyze as the occurrences of the waves that compose them are difficult to predict. However, these results must be analyzed with caution as their duration is very short (except for NOD) and the low number of visible P waves leads to suspiciously high ER.

---INSERT FIGURE 4 ---The delay between the detected P waves (TP only) and their corresponding P wave annotations have also been computed and the results are presented in table III. This delay is useful in evaluating how much the detection is disturbed according to the arrhythmia. For example, in the IVR arrhythmia, the delay between the detection and the annotation is sensibly higher than for the rest of the arrhythmias (delay = 54±33ms). This suggests that, in the proposed method, the P waves that have been classified as TP are less reliable than for other arrhythmia periods.

However, despite this isolated unreliability, the overall results deliver an accuracy comparable with inter-expert annotation variation [START_REF] Jané | Evaluation of an Automatic Threshold Based Detector of Waveform Limits in Holter ECG with the QT database[END_REF].

---INSERT TABLE 3 ---

Exploratory experiments for further extensions of the method

Other experiments have been conducted to study a possible enhancement of the algorithm by considering the QRS type (QRS classification) and the use of a QRS detector in the algorithm.

Table 4 shows the results obtained when the QRS type (human annotations) is used to prevent the search of the P wave in the case of premature ventricular contraction (PVC).

---INSERT TABLE 4 ---

As the recognition of PVC is improved, detection is enhanced in ventricular rhythms. The error is reduced for N, B and T rhythms. However, the detection is not improved in other rhythms.

QRS classification seems to be an interesting feature to exploit for P wave detection. However, while QRS detection has reached very high performance, QRS classification has not reached a sufficient accuracy to be used in practice.

Table 5 shows the results obtained when the QRS occurrences are detected using the inner QRS detector of ecgpuwave which showed a Se=99.88% and a Pr=99.97% for the detection of the R wave in another ECG database [START_REF] Jané | Evaluation of an Automatic Threshold Based Detector of Waveform Limits in Holter ECG with the QT database[END_REF].

---INSERT TABLE 5 ---Both of the detectors show an increase of errors. ecgpuwave seems to be less affected (ER=13.03% to 13.78%) than our method (ER= 7.64% to 9.13%). Periods of premature ventricular contraction (B and VT) lead to the highest increase of errors for both the detectors. However, the proposed method is still superior to ecgpuwave.

Furthermore, the P wave detection of ecgpuwave is obviously adapted to its QRS detector. Regarding the overall delay, both the methods show similar performance (ecgpuwave = 6±7ms while our method has still 8±8ms overall delay). This shows that our method is able to work with on-line QRS detector.

Discussion

Results from the literature include those of Sternickel (Sternickel,2002) with a Se between 92.68% and 99.99%. However, the method was assessed on normal sinus rhythm ECGs only (with or without effort) and without any annotation of the true P wave occurrences (if a P wave is detected in a fixed window before QRSs then it is considered as a true positive). Almeida et al. [START_REF] Almeida | Automatic delineation of T and P waves using a wavelet-based multiscale approach[END_REF] reported an Se of 98.87% on 3194 annotations but without estimation of the precision. Hernández et al. [START_REF] Hernández | Improvement of a P-wave detector by a bivariate classification stage[END_REF], compared their detector against several approaches and obtained a sensitivity of 80-90% for the records #100 of the MIT-BIH Arrhythmia Database. Their results were evaluated as being superior to those of Thakor and Yi-Sheng (Thakor and Zhu,1991) and [START_REF] Gritzali | Detection of the P and T waves in an ECG[END_REF]. [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF] reported a mean Se=99.3 and Pr=98.0 in different sinus rhythms, but the P waves of each patient were preliminary learnt. Other detectors in the literature have been evaluated on too few examples to be compared to the detector presented in this study. Some reported methods need prior learning of patient characteristics [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF][START_REF] Madhukar | Parametric technique for P wave delineation[END_REF]Pilla and Lopes,1999). This bias the detection, as the intra-patient P wave morphology variability tends to be lower than the inter-patient P wave morphology variability, which is very high. This is why most of these studies were not compared to our approach. Moreover, these techniques require that a clinician initializes the program in order to learn the P wave features of the patient. In practice, that might be too time-consuming for the medical staff, and not free of human errors that can lead to inaccurate learning. Our approach adapts itself to the patient. Indeed, the RR interval is taken into account and the PP rhythm tracking is, by definition, the PP related to the patient.

Despite the small number of available examples, the results show that the P wave detection fails in presence of certain arrhythmias. This shows that P wave detection is far from being solved in difficult ECG rhythms even if our approach can tackle some of them very well. To the best of our knowledge, a study that identifies which arrhythmias that penalise the most the P wave detection are, has never been undertaken. Along with our novel algorithm, this is one of our main contributions. In comparison, other studies generally report performance for the normal sinus rhythm case or use very little quantitative data. Presenting the results according to the context of the rhythm is also important to use the P wave detector in appropriate contexts as in the case of the cardiac monitoring system IP-Calicot [START_REF] Portet | Learning Decision Tree for Selecting QRS Detectors for Cardiac Monitoring[END_REF].

Our method is not focused on the detection of the invisible P waves. This is mainly due to two reasons. Firstly, no database containing annotated invisible P waves is available. To construct such a database, EECG or pacemaker probes should be recorded with the standard ECG and such a study is hard to set up. Consequently the validation of invisible P wave detection is a bit fuzzy [START_REF] Almeida | Automatic delineation of T and P waves using a wavelet-based multiscale approach[END_REF][START_REF] Martinez | A wavelet-based ECG delineator: evaluation on standard databases[END_REF] or reduced to the studies that have access to such material [START_REF] Jeras | Real Time P-wave Detection in Esophageal ECG[END_REF]. Secondly, the detection of hidden P waves can also be important for identification of some cases of AV dissociations, but the absence (in the ECG trace) of P wave is also informative in the diagnosis of many heart rhythms.

The aim of the proposed detector was to build a real-time, knowledge based and generic algorithm. It was not expected to perform better than detectors using QRS-T cancellation techniques. But, it appears to have similar if not better performance in practice. This can be explained by the fact that even the QRS-T cancellation techniques rely on thresholds (e.g. thresholds used to select the frequency bands in multi-resolution analysis) and thus are subject to a lack of threshold adaptation to the patient. The reason for our good performance in case of AV dissociations is due to PP rhythm tracking that searches for P waves even when no QRS is present. This is the major limitation of the P wave detection based on the QRS. Moreover, the use of knowledge about cardiology supports efficiently the detection. Another example in which knowledge is used is the detector of Dotsinsky (Dotsinsky,2007) how uses a well-known QT formula to compute the search areas and decision rules for AFL/AF recognition. (e) ecgpuwave with SVTA followed by NOD; (f) our method with SVTA followed by NOD.

Fig. 1 .

 1 Fig. 1. Overall process of the P wave detector.

Fig. 2 .

 2 Fig. 2. Area selection process.

Fig. 3 .

 3 Fig. 3. P wave detection process.

Table 3 .

 3 Median delay and standard deviation for the correctly detected P waves (TP).

	Rhythm	Duration (s)	nb P ecgpuwave	Proposed method
				Delay (ms)	Delay (ms)
	N	18421	19540	6±6	8±8
	B	1087	610	6±7	6±5
	BII	700	838	3±3	6±4
	NOD	583	40	8±18	8±15
	T	333	247	6±7	6±5
	VT	115	2	8±0	13±2
	IVR	109	14	14±27	54±33
	AB	109	60	8±18	8±7
	SVTA	59	3	8±43	6±0
	Total	21516	21354	6±6	8±8

Table 2 .

 2 Contextual results.

	Rhythm	Duration (s)	nb P		ecgpuwave			Proposed method
				ER(%)	Se(%)	Pr(%)	FM(%)	ER(%) Se(%) Sp(%) FM(%)
	N	18421	19540	8.33	95.12	96.19	95.65	5.29	97.76 96.80	97.28
	B	1087	610	35.81	90.82	68.65	78.19	16.37	98.85 84.45	91.08
	BII	700	838	50.30	49.76	99.76	66.40	27.47	72.79 99.51	84.08
	NOD	583	40	79.04	87.50	21.60	34.65	85.47	42.50 18.09	25.38
	T	333	247	16.43	94.74	87.64	91.05	11.36	97.98 90.30	93.98
	VT	115	2	96.15	100.00	3.85	7.41	96.43 100.00 3.57	6.89
	IVR	109	14	90.16	42.86	11.32	17.91	97.40	14.29 3.08	5.07
	AB	109	60	43.16	90.00	60.67	72.48	34.67	81.67 76.56	79.03
	SVTA	59	3	88.89	100.00	11.11	20.00	80.00	33.33 33.33	33.33
	Total	21516	21354	13.03	93.15	92.91	93.03	7.64	96.60 95.46	96.03

Table 1 .

 1 Global results. Se(%) Pr(%) FM(%) ER(%) Se(%) Pr(%) FM(%) Normal sinus rhythm records a 10210 0.60 99.68 99.84 99.76 0.60 99.57 99.83 99.70 Entire dataset 21354 13.03 93.15 92.91 93.03 7.64 96.60 95.46 96.03

	Records	nb P	ecgpuwave	Proposed method
	ER(%) a Records: 100, 101, 103, 117, 122

Table 5 .

 5 Contextual results using the QRS detector of ecgpuwave.

	Rhythm	Duration(s)	nb P		ecgpuwave			Proposed method
				ER(%) Se(%)	Pr(%) FM(%)	ER(%)	Se(%) Pr(%) FM(%)
	N	18421 19540	9.28	94.51	95.77	95.14	6.47	96.37 96.94	96.65
	B	1087	610	36.56	90.16	68.15	77.62	23.06	95.74 79.67	86.97
	BII	700	838	50.36	49.64 100.00	66.35	29.50	71.00 99.00	82.69
	NOD	583	40	80.24	82.50	20.63	33.01	81.13	50.00 23.26	31.75
	T	333	247	17.44	93.93	87.22	90.45	12.32	97.98 89.30	93.44
	VT	115	2	98.11	50.00	1.92	3.70	97.53 100.00	2.47	4.82
	IVR	109	14	90.32	42.86	11.11	17.65	97.44	14.29	3.03	5.00
	AB	109	60	49.49	83.33	56.18	67.11	35.53	81.67 75.38	78.40
	SVTA	59	3	93.62 100.00	6.38	11.99	80.00	33.33 33.33	33.33
	Total	21516 21354	13.78	92.53	92.67	92.60	9.13	95.19 95.25	95.22

Table 4 .

 4 Comparison of the P wave detection performance without and with the QRS type.

	Rhythm	Duration (s)	nb P	Without QRS type ER(%)	With QRS type ER(%)
	N	18421	19540	5.29	4.92
	B	1087	610	16.37	5.49
	BII	700	838	27.47	27.47
	NOD	583	40	85.47	85.47
	T	333	247	11.36	5.18
	VT	115	2	96.43	100.00
	IVR	109	14	97.40	100.00
	AB	109	60	34.67	34.67
	SVTA	59	3	80.00	80.00
	Total	21516	21354	7.64	6.41

Data

The data used for this evaluation consists of 12 two-lead records from the MIT-BIH arrhythmia database (Moody and Mark,2001) lasting about 30 minutes each with a sampling frequency of 360Hz. More precisely, records 100, 101, 103, 106, 117, 119, 122, 207, 214, 222, 223, and 231 were used. Only the upper signal (modified limb lead II), in which the P wave is visible, has been used in this study. These records present a representative variety of waveforms that an arrhythmia recognizer must deal with. Indeed, they include complex ventricular and supra-ventricular arrhythmias and conduction abnormalities that are difficult to process automatically. Globally, this dataset represents about 6 hours of ECG, 21354 beat-to-beat P wave expert annotations, which are the same as in [START_REF] Carrault | Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms[END_REF], and 12 arrhythmia types: Normal sinus rhythm (N), ventricular Bigeminy (B), ventricular Trigeminy (T), Ventricular FLutter (VFL), Ventricular Tachycardia (VT), IdioVentricular Rhythm (IVR), moBitz of type II (BII), NODal (AV junctional) rhythm (NOD), Atrial Bigeminy (AB), Atrial Fibrillation (AF), Atrial FLutter (AFL), Supra-ventricular TachyArrhythmia (SVTA). Thus, there is a good balance between normal sinus rhythms, ventricular and supra-ventricular arrhythmias.

P wave detector evaluation

The evaluation of the results of P wave detection is not straightforward. Indeed, sometimes a P wave occurrence is hidden by a QRS or a T wave on the ECG channel. Thus, some P wave detection methods that reconstruct the hidden P waves cannot estimate the detection results accurately [START_REF] Almeida | Automatic delineation of T and P waves using a wavelet-based multiscale approach[END_REF] as the hidden P waves have not been annotated by humans (because they are not visible on the ECG). In our case, we deal only with P waves that are visible. This approach makes sense since missing (hidden) P waves are also informative for diagnosing arrhythmia (the absence of P wave in a ventricular tachycardia confirms the diagnosis).

In this paper, the performance of P wave detection is assessed by computing the True Positive, TP (true detection), the False Negative, FN (missed detection) and the False Positive, FP (false detection). There is no standard for the evaluation of P wave detectors, but our approach mimics the ANSI/AAMI standard for ECG beat detector evaluation (ANSI/AAMI standard EC57:1998EC57: ,1998)). For each actual P wave annotation, a centred window of 170ms-size is defined (maximum length of a P wave). Every detected P wave that falls into one of Despite the good performance of our detector, it still needs to be improved and the combination of our approach with QRS-T cancellation applied inside the search area could improve its performance. Moreover, the parameter values of the detector are based directly on knowledge information. On-line parameter adjustment could lead to better performance.

Conclusion

In this paper, a novel approach for P wave detection is presented. The related detector uses a QRS detection based technique associated with PP rhythm tracking. This detector can run in real-time and is generic, selfadaptive to the patient's rhythm and P wave magnitude, and resistant to some atrioventricular (AV) dissociations other than atrial fibrillation and flutter.

The presented detection algorithm relies on heuristics and simple processing stages. This contrasts with current techniques which aim at using high level computing to enhance the P wave energy on the ECG and then use classification technique to detect P waves. However, despite its simplicity, our approach presents several advantages: 1) it is implementable in real-time (the whole signal is not needed before beginning the process), 2) it is human checkable (the parameter values are well known), 3) it is generic (there is no need to learn P wave features specific to the patient), 4) it adapts itself to the patient's rhythm and to the P waves magnitude, 5) it is resistant to some AV dissociations (like Mobitz type II), and 6) it has good performance.

The detector resulted in Se=97.76% and Pr=96.80% on normal sinus rhythm ECGs and Se=96.60% and Pr=95.46% on 12 ECG records of the MIT-BIH arrhythmia database containing several ventricular and supraventricular arrhythmias. The approach presented is particularly well adapted to the detection of P wave in some AV dissociations though it does not use any QRS-T cancellation technique. For example, in the case of Mobitz type II, it demonstrates Se=72.79% and Pr=99.51%. The results are presented according to each arrhythmia for an estimation of the most perturbing heart rhythms. The method has been evaluated on normal and arrhythmic ECG records and demonstrated a good ability to deal with some arrhythmias (trigeminy, bigeminy, Mobitz II). To be fair, poorer results were obtained with other arrhythmias, but this was also the case for other comparable detectors.

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