MD Maria Lesnik

PT Jose Sanchez-Guerrero

Olivier De Crouy

Camille Hervé

Joanne Guerlain

Sophie Périé
email: sophie.perie@aphp.fr

Peak inspiratory flow to predict tracheotomy in head and neck cancer: an outcome research

Keywords: Acute upper airway obstruction, inspiratory dyspnea, peak inspiratory flow (PIF), tracheostomy, head and neck cancer

Objectives: Quantitative evaluation of upper airway obstruction cannot be commonly performed in acute dyspnea, especially for head and neck cancer (HNC); decisions to control the airway, such as tracheostomy, may be difficult. Peak inspiratory flow (PIF) has been previously demonstrated as a useful tool to decide decannulation after HNC surgery. The aim of this work is to assess the role of the PIF as a standardized non-invasive tool in quantifying severe inspiratory dyspnea requiring emergency airway control.

Materials and methods:

In this monocentric pilot prospective observational study, we analyzed PIF mesures in 22 patients exhibiting dyspnea due to upper airway obstruction.

Main outcome measures: Decision to control the airway had been taken prior to PIF measurements. PIF values, measured with a handheld PIF meter (In-Check method) were registered, beside laryngeal fiberoscopy. The severity of upper airway obstruction was defined by PIF values.

Results: PIF could be performed prior to the decision of tracheostomy (imminent in 21, delayed 10 days later in one) out a total of 22 patients (21 HNC and 1 laryngeal paralysis).

PIF values below 53.1 L/min (i.e 18.3% of theoretical value) appear as values found to consider airway control in severe inspiratory dyspnea. This value is concordant with the one previously found for decision of decannulation following HNC surgery (60 L/min).

Conclusions: PIF is a non-invasive, quantitative parameter to evaluate the severity of upper airway obstruction, that may be helpful in the decision for timely tracheostomy. Testing can be performed easily, quickly and reproductively, and confirmed in larger population.

INTRODUCTION

Upper airway obstruction in head and neck cancer (HNC) can occur before, during and after treatment. Obstructive tumors (larynx, hypopharynx), laryngeal bilateral paralysis or stenosis may cause severe dyspnea, and an emergency tracheostomy may become mandatory. In addition, after treatment, a laryngeal dyspnea may be due to recurrent HNC or postradiotherapy edema. Quantification of inspiratory dyspnea and its tolerance may be difficult to assess by general practitioners, emergency physicians and radiotherapists. In addition, laryngeal fiberoscopy performed by otolaryngologists gives information about morphological features, but provides inadequate functional assessment of the laryngeal obstruction [START_REF] Cormier | Non-organic acute upper airway obstruction: description and a diagnostic approach[END_REF]. Until now, the decision for tracheostomy or controlled laryngeal intubation in HNC has commonly been taken by the otolaryngologist on the basis of clinical criteria, severity of laryngeal dyspnea [START_REF] Cros | Acute laryngeal dyspnea[END_REF] and other criteria such as a context of recurrent HNC or the patient's performance status (cardiac and pulmonary).

Since the 70s up to now, several authors use conventional spirometry to evaluate upper airway obstruction [START_REF] Miller | Evaluation of obstructing lesions of the trachea and larynx by flow-volume loops[END_REF][START_REF] Rotman | Diagnosis of upper airway obstruction by pulmonary function testing[END_REF][START_REF] Sterner | Inspiratory flow-volume curve evaluation for detecting upper airway disease[END_REF]. In these works, the most important changes were observed in the inspiratory parameters of the flow-volume loops. More specifically Peak Inspiratory Flow, Maximal Inspiratory Flow at mid-vital capacity (MIF 50) and Forced Inspiratory Volume in the first second (FIV1) have been correlated with extrathoracic airway obstruction [START_REF] Nouraei | Quantifying the physiology of laryngotracheal stenosis: changes in pulmonary dynamics in response to graded extrathoracic resistive loading[END_REF].

Guerlain et al. [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF] were the first authors who proposed a portable handheld inpiratory flow meter to evaluate upper airway obstruction, but it has also been previously reported to measure nasal obstruction [START_REF] Tsounis | The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index[END_REF]. By this method, it has been demonstrated that peak inspiratory flow (PIF) may be used as a safe and effective tool before decannulation in patients after head and neck cancer (HNC) surgery [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF]. It is a simple, inexpensive, non-invasive clinical tool, easy to use at the bedside and in the office. A PIF greater than or equal to 60 L/min, without cannula, appeared to be predictive of successful decannulation in these patients [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF], a lower PIF requiring recannulation.

Acute upper airway obstruction can occur in several clinical situations, in HNC but also in bilateral laryngeal paralysis, laryngotracheal stenosis, laryngotracheal inflammation, or after traumatic injury. Regardless of etiology, acute upper airway obstruction needs to be promptly but carefully diagnosed, evaluated and managed. The appropriate decision must be taken by otolaryngologists but emergency practitioners may firstly be concerned. Urgent tracheostomy under local anesthesia or controlled intubation for laryngeal laser debulking are the common available options to secure the airway [START_REF] Bradley | Treatment of the patient with upper airway obstruction caused by cancer of the larynx[END_REF][START_REF] Yuen | Urgent awake tracheotomy for impending airway obstruction[END_REF][START_REF] Choudhury | Endoscopic airway management of acute upper airway obstruction[END_REF].

PIF measurements can be used in acute inspiratory dyspnea, especially as criteria to guide practitioners in the tracheostomy decision making for each patient, while conventional spirometry with flow-volume loop is inappropriate in acute conditions.

The aim of this observational study is to assess the usefulness of PIF, using a handheld PIF meter (In-Check method), in quantifying severe upper airway obstruction requiring imminent airway control, and to compare with previous results of PIF in the decision of decannulation/recannulation after surgery for HNC.

PATIENTS AND METHODS

This prospective observational study was performed between November 2011 and December 2015 in our Head Neck Surgery Department. Inclusion criteria were adults who exhibited imminent upper airway obstruction and severe dyspnea, in most cases for Head Neck Squamous Cell Carcinoma (HNSCC). An emergency tracheostomy or controlled tracheal intubation with tumor debulking was discussed. Exclusion criteria were patients who had no PIF value performed before tracheostomy (no measurement of PIF, patients exhausted or unconscious due to severe dyspnea or necessitating an immediate salvage procedure were excluded from the measures/study).

Upper airway fiberoscopy was performed in all patients to qualitatively assess upper airway obstruction.

PIF was performed and recorded by the physiotherapist or by the otolaryngologist before airway control, by the method previously reported [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF], with a handheld PIF meter. PIF was measured in the sitting position. The PIF values were recorded with an officially recognized handheld inspiratory peak flow meter (In-Check oral method, HS Clement Clark International Ltd, Haag Streit Group), with a single-use mouthpiece and a nose clip. The transparent body of the In-Check device is designed to allow visual inspection before use.

Results are graduated in L/min, and the standard error of the device's measurements, according to the manufacturer, is +/-10% (i. e. 10 L/min).

When the procedure was well understood by the patients with the practitioner, the best value of at least three consecutive measures was taken [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF][START_REF] Tsounis | The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index[END_REF]. Patients inhaled with maximum effort following slow, complete exhalation.

Results were expressed at the case's patient level, by PIF value (L/min) and by the percentage of theoretical PIF value (calculated from data of Bass) [START_REF] Bass | The flow volume loop: normal standards and abnormalities in chronic obstructive pulmonary disease[END_REF].

Decision to control the airway, especially to perform tracheostomy, was taken by the otolaryngologist after physical examination including laryngeal fiberoscopy, prior to PIF measurements.

DIP mean value and standard deviations were measured. Median value, lower quartile, upper quartile and extreme values were calculated and summarized in box plots. Data were analyzed by using Microsoft Excel.

The study of PIF [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF] was approved by the Commission d'Evaluation et de Recherche Observationnelle en OtoRhinoLaryngologie (CEROL: Ethics Committee of the Society of Otolaryngology, France). Data were strictly anonymous.

RESULTS

Participants

Twenty two patients with laryngeal dyspnea were eligible during this period (Table 1).

Sixteen patients were male, six were female, and their mean age was 59.6 years with a range from 38 to 79. Patients had a HNSCC except one who had a bilateral laryngeal paralysis.

Dyspnea was related to a HNSCC (laryngeal) tumor (8 cases), occurred during chemotherapy (1 case), during postoperative HNSCC surgery in which no tracheostomy was initially performed (2 patients, with recurrent regional node HNSCC in one), followed previously treated cancer (10 cases, including 9 tumor relapse), or after laser cordotomy (1 case) (Table 1).

In recurrent or secondary HNSCC, the mean delay after initial complete treatment was 25.8 months (range from 2 to 120 months).

Tracheostomy

Tracheostomy was performed in all patients (imminent in 21 and delayed 10 days later in one), in 20 awake patients with local anesthesia, and under general anesthesia following intubation in 2 patients.

Initially, patients wore a non fenestrated cuffed tube. The cannula was usually changed the day after surgery for a fenestrated cuffless tracheostomy tube with the same diameter.

Peak Inspiratory results (Table 2) (Fig. 1-2)

In this series, before decision of tracheostomy, the mean PIF value was 53.13 L/min (range from 35 to 100, sd = 14.5), median value, lower quartile and upper quartile were respectively 50L/min, 45L/min and 60L/min (Fig. 1). The mean percentage of theoretical PIF was 18.35% (range from 11.67 to 33.33%, sd = 4.3), median value, lower quartile and upper quartile were respectively 18.33%, 16.07% and 19.86% (Fig. 2).

A PIF less or equal than 53.13 L/min (18.35% of theoretical value) appeared to be predictive of severe upper airway obstruction requiring imminent tracheostomy in these patients, except in one case (patient 16), whose tracheostomy was delayed by 10 days; this patient has decided to later perform tracheostomy near its residence, in another hospital.

One patient (case 13) exhibited a PIF value of 100 L/min, but dyspnea was due both to recurrent HNSCC with upper airway obstruction (initially treated by supracricoid laryngectomy then radiotherapy) and to acute pneumonia from laryngeal aspiration.

A total of 86% of patients exhibited a PIF ≤ 60L/min before performing tracheostomy.

Post tracheostomy period

Decannulation was performed after 3 months on the patient with bilateral laryngeal paralysis (patient 18), and on one patient who was tracheotomized at day 2 following HNSCC surgery (patient 12). Tracheostomy was maintained for 7 patients with persistent, recurrent or secondary HNSCC (patients 2, 11, 13, 15, 19, 21, 22) and in 1 patient without evidence of recurrent HNSCC (patient 16). Total pharyngolaryngectomy was performed on 6 patients with definitive tracheostomy (patients 5, 8, 9, 14, 17 and 20). Six patients died from their HNSCC (patients 1, 3, 4, 6, 7 and 10).

DISCUSSION

There are no specific guidelines in the decision making of upper airway control for obstruction. Decision is based on clinical severity criteria of dyspnea (inspiratory bradypnea, intercostal and upper sternal inspiratory depression, with or without stridor) [START_REF] Cros | Acute laryngeal dyspnea[END_REF], [START_REF] Lavanchy | Upper airway dyspnea in the adult][END_REF], and useful parameters to consider such as previous HNC and medical comorbidities (cardiac or pulmonary diseases). In fact, even severe upper airway obstruction can be well tolerated, but the exhaustion of the work of breathing can suddenly transform the tolerated dyspnea into a rapidly life-threatening condition. Tolerance of inspiratory dyspnea may vary, as breathing is frequently precarious in patients on whom surgery or radiotherapy for HNSCC has been previously performed.

Spirometric peak flow measurements represent relatively simple methods currently utilised in pneumology departments to evaluate lower airway obstruction with peak expiratory flow [START_REF] Myers | Guidelines for asthma management: a review and comparison of 5 current guidelines[END_REF][START_REF] Bateman | Global strategy for asthma management and prevention: GINA executive summary[END_REF]. Spirometric peak flow measurements are however not currently performed in surgical or emergency departments for inspiratory dyspnea.

Beside morphological and qualitative criteria for imminent airway control, PIF provides a specific quantitative criteria to predict severe upper airway obstruction, in concordance to physical examination. The level of PIF lower than about 55 L/min (i.e. 18% of theoretical value) expressed severe upper airway obstruction in this series of 22 patients. This observational study demonstrates that PIF is a non-invasive tool to measure the upper airway obstruction and to evaluate the severity of inspiratory dyspnea, helping in the decision for timely tracheostomy. This is in accordance with the PIF levels previously reported for decision of decannulation [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF] : the level to secure decannulation was found to be higher than 60L/min, a rate lower than 60L/min suggest recannulation of patients for dyspnea. As this threshold remains at a minimum level of functionnal breathing it must be re-evaluated and reconsidered for each patient, on the basis of the clinical examination and tolerance. When the PIF is close to this threshold, close watch and repeated clinical evaluations and PIF measurements must be performed. Tracheostomy was delayed by few days in patient 16, since relative tolerance of more acute dyspnea resulting from an already chronic moderate dyspnea allowed a planned tracheostomy near its residence, though the risk of respiratory decompensation. One patient required tracheostomy for dyspnea, although the PIF was higher than the values obtained in the series; however, the context of associated acute pneumonia to recurrent HNSCC with laryngeal obstruction resulted in global respiratory function failure.

Except of this patient, none of our patients presented any pulmonary or cardiac acute disease.

The physical examination and the morbidity of each patient remain essential.

In our trial, the severity of obstruction for locally advanced tumors and various comorbidities of patients did not permit controlled laryngeal intubation for tumor debulking in the 7 laryngeal cancers. It is, however an option that may always be discussed before decision of tracheostomy.

Our clinical trial also has some limitations and is an observational serie. No control population and no comparative study were possible, since acute inspiratory dyspnea always requires rapid management, and is life-threatening. However, in the follow up of patients with chronic tolerated laryngeal obstruction (laryngeal stenosis, head and neck treated patients), the value of PIF is widely higher than 60 L/min (data not yet published); however, the data collected remain selective, since all patients with moderate dyspnea are also followed by other practioners than Otolaryngologists. In addition, only two studies provide reference values for the PIF according to gender and age: the study of Bass [START_REF] Bass | The flow volume loop: normal standards and abnormalities in chronic obstructive pulmonary disease[END_REF] with 130 subjects using conventional spirometry, and the study of Tsounis [START_REF] Tsounis | The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index[END_REF] with 131 subjects using the In-check method. Data of normative PIF, with conventional spirometry and with In-check method in a population without respiratory disease (gender, age, weight) would be to consider. The standard error of the device's measurements of +/-10% (i. e. 10 L/min), must be to consider, although the best value of at least three consecutive measures was taken in this study, like in previous publications [START_REF] Guerlain | Peak inspiratory flow is a simple means of predicting decannulation success following head and neck cancer surgery: a prospective study of fifty-six patients[END_REF][START_REF] Tsounis | The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index[END_REF]. Normative data of PIF that we have also collected (data not yet published) remain higher than 150 L/min, but comparison with patients exhibiting acute upper airway obstruction remains difficult due to various results depending of gender, age and weight.

Currently, no other quantitative and reproducible thus objective scale for dyspnea is available in the context of emergency; the severity of obstruction is very subjective at laryngeal fiberoscopy, and some patients with severe upper airway obstruction may be paucisymptomatic in a steady state, but a slight imbalance can lead to severe decompensation which may quickly become life-threatening.

Scales of dyspnea, like the Dyspnea Index [START_REF] Gartner-Schmidt | Development an Validation of the Dyspnea Index (DI): A severity index for Upper Airway-related Dyspnea[END_REF] and quality of respiratory life in respiratory diseases have been validated for upper airway obstruction [START_REF] Nouraei | Sensitivity and responsiveness of the Medical Research Council dyspnoea scale to the presence and treatment of adult laryngotracheal stenosis[END_REF][START_REF] Nouraei | Validation of the Clinical COPD Questionnaire as a psychophysical outcome measure in adult laryngotracheal stenosis[END_REF], but they are inadequate in emergency situations as can be conventional spirometry, therefore there where not performed in this case serie. No ABGs (arterial blood gases) have been performed before nor after control of the airway, because saturation and capnia often remain normal in case of even acute and severe laryngeal dyspnea; in this condition, hypoxemia and/or hypercarnia occur lately and abruptly.

PIF is an objective and reproducible tool to identify patients at risk of upper airway obstruction and to establish a management plan, beside physical examination. PIF measurements can monitor the response to medical treatment in acute dyspnea (nebulisations, corticotherapy). PIF could be also available in emergency unit, in unplanned clinical presentations (laryngeal traumatic injury, neck cellulitis). Furthermore, it could also be a useful tool to decide the cadence of follow-up in chronic laryngeal disease (inflammatory laryngeal disease, laryngeal stenosis, bilateral laryngeal paralysis disease, after treatment of HNC). The learning curve is fast, and it is cost-effective. Repeated mesures of PIF, from the diagnosis of HNC, during follow up and when acute dyspnea occur, in a multicentric study, could provide a large amount of data that would refine the use of PIF. An objective and reproductible scale would allow a better understanding and management of the evolution of upper respiratoy obstruction at each step of the treatment of HNC for all practitionners, and especially younger ones.

It is also important to notice that measurements of PIF need full cooperation of the tested subject to validate the results [START_REF] Tsounis | The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index[END_REF]. Furthermore, even if the In-check method is easy to use and the learning curve is fast, a minimal practitioner´s experience is needed to confirm the validity of the values obtained. [START_REF] Bass | The flow volume loop: normal standards and abnormalities in chronic obstructive pulmonary disease[END_REF]) [START_REF] Bass | The flow volume loop: normal standards and abnormalities in chronic obstructive pulmonary disease[END_REF].

The standard error of the device's measurements, according to the manufacturer, is +/-10% (i. e. 10 L/min).

CONCLUSION

  PIF may help to evaluate acute upper airway obstruction and to optimize decision of imminent airway control, such as by tracheostomy in emergency and life-threatening situations. It may represent a specific tool in addition to clinical criteria and laryngeal fiberoscopy for routine clinic use. It is a simple, inexpensive, cost-effective, non-invasive, supplementary clinical tool that is easy to use at the bedside and in the emergency departments. The usefullness of the PIF in acute upper airway obstruction could be evaluated in a larger population.

Figure 1 :

 1 Figure 1: Box plot representing median PIF value (50L/min) and lower and upper quartile

Figure 2 :

 2 Figure 2: Box plot representing median percentage of theoretical PIF (18.35%) and lower and

Table 1 .

 1 Patient characteristics

Table 2 .

 2 PIF values PIF values expressed with minimal (min) and maximal (max) values. *dyspnea due to recurrent HNC associated with pneumonia due to aspiration, 7 months following surgery then chemoradiotherapy, ** the only patient in whom tracheostomy was delayed by10 days, ***dyspnea day one after laser cordotomy for bilateral laryngeal paralysis % theoretical PIF values calculated on basis of age and gender PIF values from

Table 1 . Patient characteristics

 1

	Mean age (n=22)	59.6 years (38-79)
	Sex (n=22)
	Male	72.7% (n=16)
	Female	27.2% (n=6)
	Etiology (n=22)
	Dyspnea in untreated or during	40.9% (n=9)
	chemotherapy
	Post-operative HNC surgery*	9.0% (n=2)
	HNC recurrence after treatment or	40.9% (n=9)
	secondary HNC
	No HNC recurrence after treatment	4.5% (n=1)
	Bilateral laryngeal paralysis	4.5% (n=1)
	Tracheostomy (n= 22)	100% (n=22)

*: Post-

operative HNC surgery associated with regional HNC recurrence in oneTable 2 . PIF values

 2

	Patients
		PIF	% theoretical PIF value
	1	55
			18.33
	2	55	18.33
	3	65	20.44

ACKNOWLEDGEMENTS

The authors are grateful to Pr Jean Lacau St Guily for his encouragement to develop this tool in the Otolaryngologist Department of Tenon Hospital, and for his helpful advice during the preparation of the manuscript.

Conflicts of interest : none


Panagiotis Moschonas
email: moschona@iti.gr

Elias Kalamaras
email: kalamar@iti.gr

Stavros Papadopoulos

Anastasios Drosou
email: drosou@iti.gr

Konstantinos Votis
email: kvotis@iti.gr

Sevasti Bostantjopoulou

Zoe Katsarou

Charalambos Papaxanthis

Vassilia Hatzitaki

Dimitrios Tzovaras
email: dimitrios.tzovaras@iti.gr

Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson's Disease

Keywords: Parkinson's Disease, Visual Analytics, Multi-objective Optimisation, Feature Discrimination Power

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Parkinson's disease (PD) is a degenerative disorder of the central nervous system that is mainly affecting the motor system. Its most obvious symptoms are movement-related, including shaking, rigidity, slowness of movement and difficulty with walking and gait [START_REF] Goetz | Movement disorder society task force report on the hoehn and yahr staging scale: status and recommendations the movement disorder society task force on rating scales for parkinson's disease[END_REF]. Tracking in a correct way the progress of the disease is crucial for the quality of patient's life, thus the need of an objective and consistent way of measuring the patient motor dexterity is essential.

There are several established scales for measuring the progress of the disease. The Unified Parkinson's Disease Rating Scale [START_REF] Ramaker | Systematic evaluation of rating scales for impairment and disability in parkinson's disease[END_REF] is the most commonly used metric for clinical study and is used as a severity rating method. An older scaling method known as the Hoehn and Yahr scale [START_REF] Goetz | Movement disorder society task force report on the hoehn and yahr staging scale: status and recommendations the movement disorder society task force on rating scales for parkinson's disease[END_REF], and a similar scale known as the Modified Hoehn and Yahr scale, have also been commonly used. The later defines five basic stages of progression. These scale measurement are derived from qualitative questionnaires asked by doctors, thus they enclose the danger of subjectivity in their results: different clinical examinations may produce different answers. Additionally, the repetition period of such solutions are in the class of several months.

Electronic devices from various domains have been also used for measuring the PD progress. The current market offers a variety of wristbands capable of measuring the tremor [START_REF] Coy | Finding the center of parkinson's disease. a novel measurement device for quantifying motor symptoms during dbs-surgery[END_REF]. However, these are used for complimentary assessment to the typical clinical examination. Moreover, their effectiveness is high when the tremor is present to the patient arm, but their efficiency is not guaranteed in early stages of the disease. An increasing set of smartphone and tablet PD applications has arisen in the market. Despite most of them are used for scheduling, there is a small subset which makes use of tests for tracking the progress of the disease. In most of these tests, the user has to use his/her hand to track a line or a shape and the software extract metrics representative of the current state. Factors such as velocity, target deviation, reaction time, minimum jerk, are recorded and compared to the ones measured from previous states. The results are displayed in curves, allowing the user to track the PD progress.

However, even providing the tests, there is still the matter of how the program is going to correlate the captured information with the disease presence on the individual. Comparing the velocity or deviation profiles per day measurements is not enough as it could not be representative of the specific patient symptoms [START_REF] Snow | Objective measures for the progression of parkinsons disease[END_REF]. For such reasons, proper visualisation capable of identifying which of the extracted features encapsulate and discriminate better the examined person status is needed. In this paper we apply a novel multi-objective visualisation for identifying which of the measured quantities are best for discriminating the presence of the Parkinson's Disease.

Relevant work

Information visualization concerns the use of interactive computer graphics to get insight into large amounts of abstract data, such as multivariate, hierarchical and network data [START_REF] Ward | Interactive data visualization: foundations, techniques, and applications[END_REF]. Conventional visualization techniques, such as bar-charts, pie-charts, and line-charts, are useful for the depiction of information of a higher level, but fail to depict large and complex data sets in detail. Therefore, and in order to enable the easy extraction of patterns, trends and outliers, a variety of novel visualization methods are continuously being developed for specific applications, e.g. [START_REF] Landesberger | Visual analysis of large graphs: state-of-the-art and future research challenges[END_REF][1] [START_REF] Carroll | Visualization and analytics tools for infectious disease epidemiology: a systematic review[END_REF].

Their ability to provide insight into large amounts of complex data make visual analytics techniques especially useful for healthcare and biomedical applications. This has resulted in numerous existing methods and tools which utilize various visualization types and user interaction levels [START_REF] Turkay | On computationallyenhanced visual analysis of heterogeneous data and its application in biomedical informatics[END_REF] [START_REF] Holzinger | Knowledge discovery and data mining in biomedical informatics: The future is in integrative, interactive machine learning solutions[END_REF]. Recent works have employed visual properties such as color and position [15][24] or animation [START_REF] Rind | Visually exploring multivariate trends in patient cohorts using animated scatter plots[END_REF], in order to visually encode patient information, group patients with similar characteristics together and discriminate between different events. Visual queries have also been utilized in combination with pattern mining and interactive visualization, in order to explore large datasets more efficiently.

The continuous development in the field of information visualization has led to a variety of new methods and techniques that enable people to understand the phenomena behind large amounts of data, and which increasingly find their way to Health and real-life applications. However, and due to their specialized nature, many of these methods have limited use outside their initially targeted application. For the majority of methods, data are considered to be homogenous and in most cases just one or two types of data are supported. In addition, data are assumed to come from a single source and to be clean and exact, whereas noisy, polluted, uncertain, and missing data are rarely dealt with. Scalability is another common issue, since it is typically limited to thousands of elements. Therefore, if one of the above assumptions is violated, standard methods from information visualization fall short, and a need for new representations arises.

Since several attributes are usually available for each patient, such as age, speed of task completion, acceleration, etc., they can be combined to provide better insights in the data. The combination of multiple sources of information for classification, clustering, visualization, etc. has generally been handled by multimodal fusion methods [START_REF] Atrey | Multimodal fusion for multimedia analysis: a survey[END_REF]. A first category of fusion methods simultaneously combine characteristics of all modalities, e.g. through weighted sums. In [START_REF] Tong | Graph based multi-modality learning[END_REF], a graph is constructed for each of multiple attributes and the graph Laplacians are then fused. Graph-based techniques are also used in [START_REF] Lin | Multiple kernel learning for dimensionality reduction[END_REF], which employs Multiple Kernel Learning [START_REF] Gönen | Multiple kernel learning algorithms[END_REF] for fusion. A second category are methods that utilize information of one modality to assist learning in another modality in an iterative manner, such as [START_REF] Blum | Combining labeled and unlabeled data with co-training[END_REF] and [START_REF] Nigam | Analyzing the effectiveness and applicability of co-training[END_REF].

A different principle is followed by works such as [11][12], where visualization of each modality is formulated as an optimisation problem and then multiobjective optimisation techniques [START_REF] Ehrgott | Multicriteria optimization[END_REF][5] are utilized to simultaneously optimize all objectives and resulting in a set of Pareto-optimal solutions, instead of one. Although such methods demonstrate the effectiveness of multimodal fusion, for a wide range of applications, they have not been adopted for visualization of healthcare data, where the combination of multiple modalities may reveal important patient behavioural characteristics that can assist decision making. In this respect, this paper presents an application of the techniques presented in [START_REF] Kalamaras | A multi-objective clustering approach for the detection of abnormal behaviors in mobile networks[END_REF] [START_REF] Kalamaras | Mova: A visual analytics tool providing insight in the big mobile network data[END_REF], in the task of clustering patients according to multiple attributes.

Applied Methodology

A set of features is first extracted by performing a simple test on a tablet device. During the test a vertical line appears at random position, while the subject has to track it as fast as possible with a stylus pen. After the test, a set of features is extracted and passed into the visual analytics engine.

The extracted features are the following: a) deviation in x axis from target, i.e. position in pixels, b) velocity, c) acceleration, and d) simulated muscle activation, extracted using methodology described in [START_REF] Stanev | Simulation and visual analysis of neuromusculoskeletal models and data[END_REF]. These features are nor-malised in the time component. This is achieved by splitting the whole recording into events which start when the target line changes its position. Then a representative signal of each feature type is extracted by averaging the event signals.

The resulted signals are fed to the Multi-objective visual analytics engine in order to analyse the discriminating power of each of the feature types.

Multi-objective Visualisation

The feature extraction procedure results in feature vectors describing various characteristics of a patient, such as age, speed of task completion, acceleration. Each of these types of types of features determines a specific type of similarities and dissimilarities among the users. An adequate visualisation of the patient data should be able to visualize these similarities. A common and straightforward visualisation scheme is to consider each patient as a point on the screen and using the relative position of the patients to denote similarity, by placing similar patients close to each other and dissimilar ones away from each other. However, the presence of multiple notions of similarity, due to the multiple types of features extracted from a single patient, renders the problem of visualizing similarities non-trivial, since many types of similarity must be visualized simultaneously, or some kind of compromise among the various feature types should be considered. In this respect, the multi-objective visualisation method of [START_REF] Kalamaras | A multi-objective clustering approach for the detection of abnormal behaviors in mobile networks[END_REF] is used hereby, which exactly considers multiple notions of similarity. The method is briefly presented in this section. The method proceeds by first considering each feature type separately, formulating visualisation as a single-objective optimisation problem, and then combining multiple objectives in a multi-objective optimisation setting.

Visualisation of a single feature type:

Let O = {O 1 , O 2 , . . . , O N } denote a set of N patients. Each patient O i is itself a set of M feature vectors: O i = {o i,1 , o i,2 , . . . , o i,M }, o i,m ∈ R Dm .
Each feature vector o i,m is a vector of dimension D m , which is specific for the m-th feature type.

Considering just a single feature type m, a distance function

d m : R Dm × R Dm → R
is used to calculate the distance between the feature vectors of two patients. Hereby, the L1 distance measure is used: where each patient O i ∈ O is represented by a vertex and there is an edge {O i , O j } ∈ E m between every pair of patients, weighted by the corresponding distance between them. As a further step, the minimum spanning tree (MST), T m (O, E m ), E m ⊂ E m , of the graph is computed, in order to reduce the number of edges, while keeping the similarity information.

d m (o i,m , o j,m ) = Dm k=1 |o i,m,k -o j,m,k | , where o i,m,k is the k-th component of vector o i,m . The distance measure is used to construct a complete graph G m (O, E m ), E m ⊆ O × O, of the patients,
The MST is then used to guide the positioning of the patient vertices on the 2-dimensional screen. Let p i = (x i , y i ) T , x i , y i ∈ R, be the coordinates of patient i on the screen. Let also P = (p 1 , p 2 , . . . , p N )

T be the matrix collecting all the 2D points as its rows. Then, the placement P of the N points is calculated by minimizing the following objective function:

J m (P) = N i=1 N j=1,j =i q 2 ||p i -p j || + i,j: (Oi,Oj )∈E m k||p i -p j || 2 .
Intuitively, this function evaluates the potential energy of the tree, if each vertex is considered as a charged particle and the edges as springs attached to pairs of them. The first term of the sum is the energy of the charges repelling one another, following Coulomb's law. The second term is the energy of the attracting springs, according to Hooke's law. If such a system of charges and springs is let to act freely, the particles will tend to repel one another, while the springs will keep together particles connected by edges, thus unfolding the tree structure. The lowest energy position is one where the tree has been completely unfolded and its structure is easily visible. In this position, the patients with similar characteristics will be put close to each other, while dissimilar patients will be put away from each other. An example of the overall procedure is illustrated in Fig. 1, where the color of each point is used to visually denote the example characteristics of each point. After the minimization of the potential objective, in Fig. 1(c), the structure of the tree is apparent and similar colors are placed close to each other.

Handling Multiple Feature Types: The above procedure holds for a specific feature type m. If a different feature type is used, different distances will be calculated among the points, resulting in a different tree, a different objective function and a different ultimate positioning. The multiple feature types result in a set of objective functions J (P), instead of a single one: J (P) = {J 1 (P), J 2 (P)), . . . , J M (P)} , so that the goal is to minimize them all simultaneously:

P opt = arg min P J (P).
The multiple objective J m are conflicting, since the optimal solution for one of them is not optimal for another, thus a single solution cannot in general be reached. Such problems of conflicting objectives are handled by multi-objective optimisation techniques, which result in a set of optimal trade-offs among the objectives, namely the Pareto set. Multi-objective optimisation is based on the notion of Pareto dominance among the feasible solutions. A solution dominates another one if it has a smaller value for at least one objective and there is no objective for which it has a larger value. Between two solution, the dominant one is preferred, since it is impartially better than the other one, with respect to all objective functions, without sacrificing any of them. Formally, a solution P 1 dominates another solution P 2 , if J m (P 1 ) ≤ J m (P 2 ), ∀m ∈ {1, . . . , M }, and ∃k ∈ {1, . . . , M } : J k (P 1 ) < J k (P 2 ).

If two solutions mutually do not dominate each other, they are said to be incomparable, since there can be no impartial judgment as to which is better than the other. The goal of multi-objective optimisation is to compute the set of solutions that dominate all other feasible solutions but are mutually incomparable. This set is called the Pareto set and consists a set of optimal trade-offs among the multiple objectives. In Fig. 2, the feasible and optimal solutions for an example problem of two objectives are depicted.

The gray-shaded area represents the set of all feasible solutions, while the bold border in the lower left of the feasible area represents the solutions of the Pareto set. In such diagrams in the space of the objective function values, the solutions corresponding to the Pareto set are called the Pareto front. Three example solutions are shown. Solution P 2 dominates P 1 since both objectives have smaller values at P 2 . Similarly, P 2 dominates all solutions within the hatched area. On the other hand, solutions P 2 and P 3 are incomparable, since none dominates the other. All the solutions of the Pareto front are mutually incomparable, since decreasing one objective leads to increasing the other.

Computing the Pareto front, which is the goal of multi-objective optimisation, means presenting the decision maker with a minimal set of optimal tradeoffs, from which to select. In this paper, this set of solutions corresponds to different trade offs among the various medical and simulated features describing the patients. By selecting among the trade-offs, through an application interface, the doctor or any decision maker can put more focus on various characteristics and thus view different kinds of relationships and groupings among the data.

Preliminary Results

A total of 42 PD patients of various ages (mean: 63, stdev: 8.1) and 10 healthy / control subjects of matching ages (mean: 64.9 , stdev: 7.75) were measured. All PD subjects were tested with an on-medication treatment state. The subjects performed the test with both hands and the extracted features were averaged, thus resulting into one feature-set per subject. The proposed algorithm computes the visualisation output of the 52 instances almost instantly (t < 0.05sec). The interactive environment allows its user to re-adjust the objective weights and updates the dots position without any lag. In terms of scalability the algorithm performs at O(N log N ), where N is the number of nodes. Experiments in the application domain of mobile network security have shown that a dataset of 4800 points needs less than 3ecs for the output solution [START_REF] Kalamaras | Mova: A visual analytics tool providing insight in the big mobile network data[END_REF]. The aforementioned experiments were performed using a 8-threaded Intel Core i7 processor running at 4GHz.

The patient space of the combining all the features, using equal weighting among them, is depicted in Fig. 3. Green coloured dots represent a control subject, while yellow (mild condition), orange (medium condition) or red (severe condition) dots represent the PD patients. As it is shown, a combination of all the features results into a sparse cluster of the green dots. However, it fails to cluster three of the control subjects. This fact can be translated as that the selected combination of the feature types does not provide enough discriminating power. By adjusting the weights among the feature types, the most crucial one for separating the healthy from the patient cohort may be found. In Fig. 4, the feature representing the extracted Muscle Activations separates the two groups in much more robust way, thus, defining it as the most significant for this type of application. Other features have been tested, but none has provided such good discrepancy.

Conclusion

A visual analytics methodology based on multi-objective optimisation was applied to a typical PD line tracking test output and managed to identify successfully the most crucial feature able to separate the healthy from the patient groups. In the future work, there are plans of investigating the potential of the methodology with other neurodegenerative genetic disorders, such as the Huntington's Disease.

Fig. 1 .

 1 Fig. 1. Example of the potential objective minimization. (a) The points and their similarities form a complete graph. The graph's vertices are placed at random positions. (b) The minimum spanning tree of the graph is calculated. (c) By minimizing the potential objective, the vertices are moved so that the tree's structure is apparent.

Fig. 2 .

 2 Fig. 2. Example Pareto diagram illustrating the Pareto front for a problem of two objectives, J1 and J2. The gray area represents the set of all feasible solutions, while the bold border is the Pareto front. Solution P2 dominates P1, as well as all solutions within the hatched area. Solutions P2 and P3 are incomparable.

Fig. 3 .

 3 Fig. 3. Multi-objective result of all the feature type combined. The gathering of the green dots representing the control subjects in the lower part is not concentrated.

Fig. 4 .

 4 Fig. 4. Testing the impact of each feature: increasing the weight of the "Muscle Activation" feature results into a robust patient-healthy separation.

Acknowledgement: Partial funding for this work was provided by the EU co-funded project NoTremor (EC FP7 Grant agreement no 610391).