N Houmani
email: nesma.houmani@telecom-sudparis.eu

H Bakardjian

A Le Montagner

J Mariani

J Boudy

K Kinugawa

B Dubois

Multi-dimensional profiling of elderly at-risk for Alzheimer's disease in a differential framework

Keywords: clinical EEG data, Alzheimer's disease, prediction, diagnosis, cognitive profiling

The utility of EEG in Alzheimer's disease (AD) research has been demonstrated over several decades in numerous studies. EEG markers have been employed successfully to investigate AD-related alterations in prodromal AD and AD dementia. Preclinical AD is a recent concept and a novel target for clinical research. This project tackles two issues: first, AD prediction at the preclinical stage, by exploiting the multimodal INSIGHT-preAD database, acquired at the Pitié-Salpetrière Hospital; second, an automatic AD diagnosis in a differential framework, by exploiting another large-scale EEG database, acquired at Charles-Foix Hospital. In this project, we will investigate AD predictors at preclinical stage, using EEG data of only subjective Memory Complainers in order to establish a cognitive profiling of elderly at-risk. We will also identify EEG markers for AD detection at early stages in a differential diagnosis context. The correlation between EEG markers and clinical biomarkers will be also assessed for a better characterization of the retrieved profiles and a better understanding on the severity of the cognitive disorder. The exploited large-scale complementary data offer the opportunity to investigate the full spectrum of the AD neuro-degeneration changes in the brain, using a big data approach and multimodal patient profiling based on resting-state EEG markers.

I. INTRODUCTION

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that leads to progressive decline of cognitive functions, along with behavioral disturbances and insidious loss of autonomy in daily life activities. Its incidence increases exponentially with age, and doubles every 5 years after the age of 65 [START_REF] Kukull | Dementia and Alzheimer disease incidence: a prospective cohort study[END_REF][START_REF] Qiu | Epidemiology of Alzheimer's disease: occurrence, determinants, and strategies toward intervention[END_REF][START_REF] Corrada | Dementia incidence continues to increase with age in the oldest old: the 90+ study[END_REF], being the most common cause of dementia in Western societies. As the world population ages, the frequency is expected to double by 2030 and triple by 2050 [START_REF]incidence, cost and trends, Alzheimer's disease International[END_REF]. Accordingly, and because of the unprecedented level of aging in the world, the health care costs associated with AD are exceptionally high, imposing an important burden on modern societies.

The evolution of AD follows five stages. The "preclinical" stage is asymptomatic, but the brain lesions of AD are present. At this stage, the concept of Subjective Cognitive Impairment (SCI) has been proposed recently, defined by a self-experienced persistent decline in cognitive capacity in comparison with a previously normal status [START_REF] Jessen | A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease[END_REF]. These subjective complaints are considered as a risk factor for AD [START_REF] Mitchell | Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis[END_REF][START_REF] Dubois | Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria[END_REF]. Then, in "Mild Neurocognitive Disorders" (MND) stage, patients exhibit measurable memory impairments, but maintain their functional capacities [START_REF] Dubois | Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria[END_REF][START_REF] Petersen | Mild cognitive impairment: ten years later[END_REF]. In the "Mild AD" stage, cognitive deficits are more notable, such as memory and learning impairments. These syndromes become more severe in the "Moderate AD" stage and in the final "severe" stage of the disease, almost all cognitive and motor functions are significantly deteriorated and patients lose autonomy becoming completely dependent on caregivers. The average duration of survival of AD patients is 5-8 years after clinical diagnosis [START_REF] Helzner | Survival in Alzheimer disease: a multiethnic, population-based study of incident cases[END_REF].

In the initial stages of the disease, AD targets limbic regions that subserve episodic memory, leading to a relatively circumscribed inability to learn and retain new information. Also, a disruption of fronto-hippocampal connections has been found to occur from the early stages of AD, in parallel with hippocampal atrophy, and it has been reported that it may specifically contribute to the initial memory impairment in AD patients [START_REF] Rémy | White matter disruption at the prodromal stage of Alzheimer's disease: relationships with hippocampal atrophy and episodic memory performance[END_REF]. Over time, AD spreads to other neocortical regions [START_REF] Braak | Neuropathology of Alzheimer's disease: what is new since A. Alzheimer?[END_REF], affecting additional spheres of cognition, such as executive functions, attention, visuospatial abilities and language. Characteristic neuropathological findings include selective neuronal and synaptic losses [START_REF] Morrison | Life and death of neurons in the aging brain[END_REF], extracellular neuritic plaques containing the b-amyloid peptide [START_REF] Wong | Neuritic plaques and cerebrovascular amyloid in Alzheimer disease are antigenically related[END_REF] and neurofibrillary tangles composed of hyperphosphorylated forms of the tau protein [START_REF] Delacourte | Alzheimer's disease: Tau proteins, the promoting factors of microtubule assembly, are major components of paired helical filaments[END_REF].

No medication exists to stop or reverse the disease progression, and many therapeutics trials failed and their costeffectiveness has been questioned [START_REF] Loveman | The clinical and cost-effectiveness of donepezil, rivastigmine, galantamine and memantine for Alzheimer's disease[END_REF][START_REF] Knapp | Cost-effectiveness of donepezil and memantine in moderate to severe Alzheimer's disease (the DOMINO-AD trial)[END_REF]. Current major anti-AD medication trials are focusing on "preclinical" stage, in order to treat before symptoms development. Therefore, AD detection at MND and preclinical stages becomes an important issue for the scientific community.

Besides, there are currently no specific markers that can confirm the AD diagnosis with sufficient certainty, especially in the early stages. Thus, there is a crucial need for real advances to identify reliable AD markers for profiling elderly at-risk, diagnosis and monitoring disease progression. New approaches to these complex challenges require a mix of resources and skill sets from different disciplines to enhance and forward AD research. This is the objective of our collaborative research work that we present in this paper. The project started in December 2018 and involves three French partners: two hospitals from AP-HP in France, Salpêtrière Hospital and Charles-Foix Hospital, and Samovar Laboratory of Télécom SudParis. This paper offers an insight on current transdisciplinary research, including data analytics computing and clinical data fusion, for prognosis, diagnosis, and monitoring the evolution of AD. By relying on signal processing, data analytics and machine learning techniques, we target to identify neuroimaging-and new neuro-dynamic biomarkers correlated with AD, which contribute to a better understanding of the brain dynamics of AD patients, and improve the prediction of elderly at-risk and the reliability of AD diagnosis. This research project concerns the multi-dimensional profiling of elderly at-risk for AD in a differential framework, and exploits two complementary large-scale EEG databases, acquired at Salpêtrière and Charles-Foix Hospitals. Télécom SudParis team is in charge of processing and analyzing these two databases by applying signal processing methods, statistical and machine learning techniques. Such analyses will be based on the clinical expertise of the medical staff.

II. STATE OF THE ART

The prognostic utility of electroencephalography (EEG) in Alzheimer's disease (AD) research has been demonstrated over several decades in numerous studies [START_REF] Jackson | Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease[END_REF][START_REF] Rodriguez | Brain functional network in Alzheimer's disease: diagnostic markers for diagnosis and monitoring[END_REF][START_REF] Lizio | Electroencephalographic rhythms in Alzheimer's disease[END_REF][START_REF] Takahashi | Complexity of spontaneous brain activity in mental disorders[END_REF][START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF][START_REF] Abásolo | Entropy analysis of the EEG background activity in Alzheimer's disease patients[END_REF][START_REF] Bock | Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy[END_REF][START_REF] Escudero | Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy[END_REF][START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF].

EEG is a non-invasive, relatively inexpensive, and potentially mobile technology with high temporal resolution (on the order of milliseconds). It was mainly investigated as a tool for AD diagnosis, by comparing EEG recordings of AD patients only to those of control subjects (healthy subjects) [START_REF] Alberdi | On the early diagnosis of Alzheimer's disease from multimodal signals: A survey[END_REF][START_REF] Malek | Electroencephalographic markers in dementia[END_REF][START_REF] Gouw | EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects[END_REF][START_REF] Dauwels | Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?[END_REF][START_REF] Dauwels | Diagnosis of Alzheimer's disease from EEG signals: where are we standing?[END_REF][START_REF] Vecchio | Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review[END_REF].

The measurable electrophysiological changes may be due to loss of gray matter and, more importantly, loss of neuronal connections [START_REF] Blennow | Alzheimer's disease[END_REF]. EEG biomarkers have been employed successfully to investigate AD-related alterations in the brain dynamics with two main approaches -using spontaneous resting-state EEG (rsEEG) and functional EEG (fEEG) based on event-related responses (ERP). Several brainwave components of the rsEEG could be altered in various stages of AD.

Evidence from a number of studies indicates that AD leads to a reduction in the complexity of EEG signals and changes in EEG synchrony, in prodromal AD and AD dementia. These modifications in EEG recordings have been used as discriminative features for AD diagnosis. Several methods were developed for assessing the complexity of EEG signals. The correlation dimension and the first positive Lyapunov exponent were frequently used [START_REF] Takahashi | Complexity of spontaneous brain activity in mental disorders[END_REF][START_REF] Gouw | EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects[END_REF][START_REF] Adeli | Alzheimer's disease: models of computation and analysis of EEGs[END_REF][START_REF] Jelles | Decrease of nonlinear structure in the EEG of Alzheimer patients compared to healthy controls[END_REF][START_REF] Jeong | Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension[END_REF][START_REF] Jeong | Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia[END_REF]. It was found that EEG signals from AD patients exhibit lower values of such measures than signals from healthy subjects. Other information-theoretic methods, entropy-based approaches in particular, have emerged as potentially useful EEG markers of AD [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF][START_REF] Bock | Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy[END_REF][START_REF] Escudero | Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy[END_REF]: epochbased entropy [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF], sample entropy [START_REF] Abásolo | Entropy analysis of the EEG background activity in Alzheimer's disease patients[END_REF], Tsallis entropy [START_REF] Bock | Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy[END_REF], approximate entropy [START_REF] Abásolo | Analysis of regularity in the EEG background activity of Alzheimer's disease patients with approximate entropy[END_REF][START_REF] Pincus | Approximate entropy as a measure of irregularity for psychiatric serial metrics[END_REF], multi-scale entropy [START_REF] Escudero | Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy[END_REF], and Lempel-Ziv complexity [START_REF] Abásolo | Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and Central Tendency Measure[END_REF].

Since AD is hypothesized to induce functional disconnection between brain regions, other works focused on synchrony changes detection [START_REF] Lizio | Electroencephalographic rhythms in Alzheimer's disease[END_REF][START_REF] Takahashi | Complexity of spontaneous brain activity in mental disorders[END_REF][START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF] between pairs of EEG signals. A large variety of measures has been developed to quantify EEG synchrony: correlation coefficient [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF], coherence [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Escudero | Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms[END_REF][START_REF] Sankari | Wavelet coherence model for diagnosis of Alzheimer's disease[END_REF], Granger causality [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Babiloni | White-matter lesions along the cholinergic tracts are related to cortical sources of EEG rhythms in amnesic mild cognitive impairment[END_REF], phase synchrony [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Czigler | Quantitative EEG in early Alzheimer's disease patients-power spectrum and complexity features[END_REF][START_REF] Park | Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease[END_REF], state space-based synchrony [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Czigler | Quantitative EEG in early Alzheimer's disease patients-power spectrum and complexity features[END_REF][START_REF] Kramer | Synchronization measures of the scalp EEG can discriminate healthy from Alzheimer's subjects[END_REF], stochastic event synchrony [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Sankari | Wavelet coherence model for diagnosis of Alzheimer's disease[END_REF][START_REF] Park | Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease[END_REF][START_REF] Dauwels | Quantifying statistical interdependence by message passing on graphs, Part I: onedimensional point processes[END_REF][START_REF] Dauwels | Quantifying statistical interdependence by message passing on graphs, Part II: multidimensional point processes[END_REF] and mutual information [START_REF] Jeong | Mutual information analysis of the EEG in patients with Alzheimer's disease[END_REF]. All these studies reported decreased EEG synchrony in MND and AD patients compared to healthy subjects.

To discriminate AD patients from healthy subjects, all the above-mentioned studies analyzed the EEG signals either in the time domain or in specific standard frequency bands: 0.1-4Hz (delta band), 4-8Hz (theta band), 8-12Hz (alpha band), 12-30Hz (beta band) and 30-100Hz (gamma band) [START_REF] Jeong | Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia[END_REF][START_REF] Kropotov | Quantitative EEG, Event-Related Potentials and Neurotherapy[END_REF] or in the whole frequency range between 4 and 30Hz [START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF].

Spectral analysis studies reported that, in prodromal AD and AD dementia, there is an enhancement of low-frequency waves in the theta [START_REF] Grunwald | Correlation between cortical theta activity and hippocampal volumes in health, mild cognitive impairment, and mild dementia[END_REF] and delta bands in the temporal and occipital lobes, as well as a reduction of higher-frequency beta power in the temporal and occipital regions [START_REF] Jelic | Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer's disease[END_REF][START_REF] Coben | A longitudinal EEG study of mild senile dementia of Alzheimer type[END_REF][START_REF] Soininen | Longitudinal EEG spectral analysis in early stage of Alzheimer's disease[END_REF] and a decline in alpha band (8-12 Hz) power [START_REF] Gouw | EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects[END_REF][START_REF] Luckhaus | Quantitative EEG in progressing vs stable mild cognitive impairment (MCI): results of a 1-year follow-up study[END_REF][START_REF] Babiloni | Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study[END_REF][START_REF] Nunez | Electric Fields of the Brain: the Neurophysics of EEG[END_REF]. A reduced spectral coherence between the two hemispheres was also shown between alpha and beta frequency bands [START_REF] Besthorn | EEG coherence in Alzheimer disease[END_REF][START_REF] Dunkin | Reduced EEG coherence in dementia: state or trait marker?[END_REF][START_REF] Locatelli | EEG coherence in Alzheimer's disease[END_REF]. These spectral differences were also shown to be correlated with the severity of the disease [START_REF] Besthorn | EEG coherence in Alzheimer disease[END_REF][START_REF] Hughes | The relationship between EEG changes and cognitive functions in dementia: a study in a VA population[END_REF][START_REF] Sloan | Neurophysiology and SPECT cerebral blood flow patterns in dementia[END_REF]. Moreover, alpha rhythms are usually distributed in the occipital area for healthy subjects; in AD patients, they increasingly move towards anterior areas as the disease progresses [START_REF] Babiloni | Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study[END_REF][START_REF] Claus | Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease[END_REF][START_REF] Kowalski | The diagnostic value of EEG in Alzheimer disease: correlation with the severity of mental impairment[END_REF]. Early stages of AD have been associated with an increase of theta activity and/or a decrease of alpha activity. In more severe stages of Alzheimer's disease, an increase of both theta and delta activities has been observed together with a decrease of both alpha and beta activities, additionally to a reduction in the amplitude of the peak of alpha frequency band [START_REF] Ihl | Topography of the maximum of the amplitude of EEG frequency bands in dementia of the Alzheimer type[END_REF][START_REF] Vialatte | Improving the Specificity of EEG for Diagnosing Alzheimer's Disease[END_REF]. In all these studies, a 70%-85% correct detection rate is commonly achieved for different degrees of disease severity.

All the above-mentioned investigations have some limitations: This project takes advantage of two large-scale EEG databases, acquired at two clinical centers of AP-HP: The multimodal INSIGHT-preAD database and the Charles-Foix database.

(i) First,

IV. DESCRIPTION OF THE TWO CLINICAL EEG DATABASES

These two unique large-scale complementary databases offer the opportunity to investigate the full spectrum of the AD neuro-degeneration changes in the brain, using a big-data approach and multimodal patient profiling with a major role for resting-state EEG markers.

A. The multimodal INSIGHT-preAD database

INSIGHT-preAD (INveStIGation of AlzHeimer's PredicTors in Subjective Memory Complainers

) is an on going and monocentric cohort study from the Salpêtrière Hospital, Paris, France, which started in 2013 [START_REF] Dubois | Cognitiveand neuroimaging features and brain beta-amyloidosis in individuals at riskof Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study[END_REF]. The cohort includes cognitively normal individuals, over 70 years, with subjective memory complaints (SMC) but normal cognitive and memory scores according to the Mini Mental State Examination (MMSE ≥ 27), Clinical Dementia Rating (CDR = 0) and Free and Cued Selective Reminding Test (Total Recall ≥ 41).

Subjects were stratified by brain amyloid status (amyloid positive or amyloid negative) according to the uptake of 18F-Florbetapir. Demographic, cognitive, psycho-behavioral, functional, ApoE status, MRI (anatomical, diffusion, resting state-fMRI, arterial spin labeling sequences), FDG-PET imaging, EEG recordings with resting state and ERP, were performed at baseline with optional Actigraphy and CSF investigations.

All subjects participated in follow-up with neuropsychological assessment, EEG, and Actigraphy every year; blood samplings for research on biomarkers, MRI, FDG-PET and amyloid-PET scans every 2 years. The clinical staff investigated the association between amyloid status and the assessed measures at baseline and month 24, and assessed the clinical status of participants at month 30 to identify the factors associated with progression to prodromal AD.

At baseline, the database contains EEG data of 318 elderly Subjective Memory Complainers, which had a mean age of 76.03 (SD 3.47) years with a mean MMSE score of 28.67 (SD 0.96) and a high educational level (6.19 [SD 2.05] on a scale of 1-8).

EEG data were recorded using a high-density 256 channel EGI system (Electrical Geodesics Inc., USA) with a sampling rate of 250 Hz. The electrodes used are sponge-based in order to have a quick application time (10-20 min) due to the elderly population. During the recording, patients were instructed to keep awake and relaxed, with their eyes closed in a quiet room. 60 s of eyes-closed resting-state were selected for analysis.

Due to the availability of resting-state EEG and other multimodal information, such as amyloid-PET SUVr measures and an extensive battery of neuropsychological tests, it is possible to analyze the neuro-dynamics profiles of individuals at-risk for AD or in the preclinical AD stage by using different advanced signal analysis methods.

B. The Charles-Foix database

This database was recorded in real clinical conditions between 2009 and 2013 at Charles-Foix Hospital (Ivry-sur-Seine, France). The EEG recordings were obtained at rest and with closed eyes using a Deltamed digital EEG acquisition system for a minimum of 20 minutes.

Scalp electrodes were placed according to the modified International 10-20 system with 11 additional electrodes in a common reference montage using a sampling rate of 256 Hz. Thirty electrodes were placed on the scalp: Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FC7, FC4, FT8, T3, C3, Cz, C4, T4, TP7, CP3, CPz, CP4, TP8, T5, P3, Pz, P4, T6, O1, Oz, O2.

The patients who complained of memory impairment were referred to the outpatient memory clinic of the Charles-Foix Hospital where they underwent a battery of tests for brain disorders, including neuropsychological test, brain imaging and blood samplings. Patients with epilepsy were excluded. Each patient was given a diagnosis at the memory clinic on the basis of the clinical, brain imaging, psychometric findings, and discussions held by a multidisciplinary medical team, using the standard diagnostic criteria: DSM-IV, NINDS, Jessen criteria for SCI, Mc Keith criteria for Lewy body dementia [START_REF] Jessen | A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease[END_REF][START_REF] Mitchell | Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis[END_REF]. We didn't use EEG recordings to establish the diagnosis.

In this project we will pursue our previous small-scale study [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF] on a part of this database. Indeed, in [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF] In this project, we will extend our study on new EEG data collected at Charles-Foix Hospital from 2013, as the first data collected between 2009 and 2013 were analyzed in our previous study [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF].

The database reflects what medical practitioners are facing in reality, as opposed to databases used in the literature [START_REF] Takahashi | Complexity of spontaneous brain activity in mental disorders[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF][START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Jeong | Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia[END_REF][START_REF] Escudero | Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms[END_REF][START_REF] Sankari | Wavelet coherence model for diagnosis of Alzheimer's disease[END_REF][START_REF] Babiloni | White-matter lesions along the cholinergic tracts are related to cortical sources of EEG rhythms in amnesic mild cognitive impairment[END_REF][START_REF] Czigler | Quantitative EEG in early Alzheimer's disease patients-power spectrum and complexity features[END_REF][START_REF] Park | Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease[END_REF] that are prone to experimental constraints that do not match the reality on the ground.

V. SCIENTIFIC CONTENT

The main research approaches for this project will be rsEEG feature extraction with signal processing techniques, unsupervised clustering methods to retrieve automatically cognitive profiles, data fusion, and supervised clustering techniques for AD diagnosis in a differential framework.

As mentioned in Section II, several EEG markers were developed in the literature for AD detection. These markers will be transposed to the framework of this project and applied as biomarkers for profiling elderly at-risk for AD in a differential framework. At each step of our experiments, several EEG markers will be evaluated, as well as available clinical data. Their fusion will be also investigated to characterize AD and elderly at-risk of AD. This research project is organized in two parts: (i) AD prognostic (prediction) at the preclinical stage using the multimodal INSIGHT-preAD database; (ii) AD diagnosis in a differential framework, using the Charles-Foix database. Experimentally, our findings on Charles-Foix database will be exploited to address the issues tackled by INSIGHT-preAD database.

A. INSIGHT-PreAD study: AD prognostic / prediction at the preclinical stage

Preclinical AD is a relatively recent concept and a novel target for clinical research, especially in the field of EEG. Due to the availability of rsEEG and other multimodal information in the INSIGHT-preAD study, it is possible to study in detail the neurodynamics profiles of individuals at risk for AD or in the preclinical AD stage by using different advanced signal analysis methods.

This study was recently approved by the scientific committee of the Salpêtrière hospital. In this project, we aim to implement and investigate this concept using large-scale rsEEG feature extraction and fusion, in order to minimize the effects of intersubject variability and subjective signal feature pre-selection, and to maximize the effectiveness of our predictive procedure based on prognostic multi-dimensional EEG profiling.

The feature extraction involves various EEG markers whose effectiveness was already established in the literature, as synchrony measures, complexity-based measures, and nonlinear measures [START_REF] Takahashi | Complexity of spontaneous brain activity in mental disorders[END_REF][START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF][START_REF] Abásolo | Entropy analysis of the EEG background activity in Alzheimer's disease patients[END_REF][START_REF] Bock | Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy[END_REF][START_REF] Escudero | Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy[END_REF][START_REF] Dauwels | A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG[END_REF][START_REF] Blennow | Alzheimer's disease[END_REF][START_REF] Gouw | EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects[END_REF][START_REF] Adeli | Alzheimer's disease: models of computation and analysis of EEGs[END_REF][START_REF] Jelles | Decrease of nonlinear structure in the EEG of Alzheimer patients compared to healthy controls[END_REF][START_REF] Jeong | Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension[END_REF][START_REF] Jeong | Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia[END_REF][START_REF] Abásolo | Analysis of regularity in the EEG background activity of Alzheimer's disease patients with approximate entropy[END_REF][START_REF] Pincus | Approximate entropy as a measure of irregularity for psychiatric serial metrics[END_REF][START_REF] Abásolo | Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and Central Tendency Measure[END_REF][START_REF] Escudero | Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms[END_REF][START_REF] Sankari | Wavelet coherence model for diagnosis of Alzheimer's disease[END_REF][START_REF] Babiloni | White-matter lesions along the cholinergic tracts are related to cortical sources of EEG rhythms in amnesic mild cognitive impairment[END_REF][START_REF] Czigler | Quantitative EEG in early Alzheimer's disease patients-power spectrum and complexity features[END_REF][START_REF] Park | Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease[END_REF][START_REF] Kramer | Synchronization measures of the scalp EEG can discriminate healthy from Alzheimer's subjects[END_REF][START_REF] Dauwels | Quantifying statistical interdependence by message passing on graphs, Part I: onedimensional point processes[END_REF][START_REF] Dauwels | Quantifying statistical interdependence by message passing on graphs, Part II: multidimensional point processes[END_REF][START_REF] Jeong | Mutual information analysis of the EEG in patients with Alzheimer's disease[END_REF].

In order to retrieve homogeneous subgroups from the entire available population, unsupervised clustering techniques, using multi-dimensional feature inputs, are applied. Each obtained group will be associated with a given clinical profile.

The relationship between the obtained profiles and various clinical data (brain amyloid β deposition), socio-demographic data (age, education level), and neuropsychological exam scores (FCSRT, MMSE, DMS48, Rey figure, DO80, etc) will be assessed. This will give us a new insight on the retrieved profile characterization of individuals at-risk of AD.

For our study, both eyes-closed and eyes-open rsEEG data will be used for profiling elderly at-risk for AD, due to the often complementary brain processes involved in these two resting states.

The longitudinal study, at M24 and M30, is a key point for this study in order to assess the evolution of the profiles to clinical AD and to identify the markers associated to the profiles that are at-risk for AD.

It is worth noticing that our scientific approach for AD prognostic study will be based on our expertise and findings on previous databases, especially on Charles-Foix database that contains different groups of patients: SCI, MND and AD [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF]. Concerning EEG markers, we will focus on phase synchrony measure, bump modeling, parameters derived from graph theory, and Epoch-based Entropy measure that exploits a statistical modeling of the quasi-stationary and multidimensional EEG signal with Hidden Markov Models.

B. Charles-Foix study 2: AD diagnosis in a differential framework

This study was approved by the local ethical committee of Paris 6. This study will exploit the data collected between 2009 and 2013 that were analyzed in our previous small-scale study [START_REF] Wong | Neuritic plaques and cerebrovascular amyloid in Alzheimer disease are antigenically related[END_REF], and the new EEG data collected at from 2013.

In a first step, our methodology proposed in [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF] will be applied on the new EEG data, in blind manner, to estimate its generalization capacity for AD diagnosis. Our proposal in [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF] exploits two features: entropy-based complexity measure [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF][START_REF] Houmani | Stationary Epoch-based Entropy Estimation for Early Diagnosis of Alzheimer's Disease[END_REF][START_REF] Houmani | Epoch-based Entropy for Early Screening of Alzheimer's Disease[END_REF] and synchrony measure [START_REF] Vialatte | A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics[END_REF][START_REF] Vialatte | Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals[END_REF], both computed in different frequency ranges and for different brain regions. The most relevant measures and the most relevant frequency range were selected with the Orthogonal Forward Regression (OFR) algorithm and the random probe method [START_REF] Stoppiglia | Ranking a random feature for variable and feature selection[END_REF][START_REF] Chen | Orthogonal least squares methods and their application to nonlinear system identification[END_REF][START_REF] Dreyfus | Neural Networks: methodology and applications[END_REF] to improve the accuracy of EEG classification using a Support Vector Machine classifier (SVM) [START_REF] Campbell | Learning with Support Vector Machines[END_REF][START_REF] Boser | A training algorithm for optimal margin classifiers[END_REF].

Our previous study didn't focus on MND; this was a drawback of our previous published work [START_REF] Houmani | Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework[END_REF]. Indeed, as shown in Table 1, only 60.3% of MCI patients are well detected, and among the misclassified MND, 70% of them are considered as patients with other pathologies. Thus, we propose, in a second step, to apply other EEG markers in order to find those that characterize MND group and to evaluate the classification accuracy of each group of patients using supervised clustering techniques with probabilistic outputs. The probabilistic approach is more suitable for decision-making to experts. We will evaluate the performance of our proposal in terms of specificity and sensitivity.

In a third step, instead of assuming a homogeneous behavior of each population (SCI, MND, possible AD, and patients with other pathologies) like mostly done in the literature, we propose to exploit unsupervised clustering techniques, in a multimodal framework, on EEG markers to uncover homogeneous subgroups from all the population, some of which possibly associated with a given cognitive profile.

Table 1: Confusion matrix for AD diagnosis with 4 groups.

The relationship between the obtained profiles and various clinical data (MMSE, RL/RI-16) will be assessed, which will offer an insight on the severity of the cognitive disorder and the diagnosis of the disease.

Finally, the continuum of the progression towards dementia will be studied, by analyzing the evolution of EEG markers and clinical data in a transversal way from SCI, MND to AD, considering the retrieved cognitive profiles.

VI. EXPECTED IMPACT

This collaborative research at the regional level may facilitate further insight into largely unknown phenomena during the progression of AD neuropathology, with a focus on an integrative view of the disease, including the extension of new knowledge on the cognitive and brain compensation in its early stages.

Another expected outcome is the development of an automatic diagnostic aid-method that will provide a risk indicator for AD to the medical doctors at the preclinical stage.

Besides, the idea behind this research work is to highlight the use of EEG for AD diagnosis. This non-invasive and inexpensive technology (20 to 100 times cheaper than a brain imaging test) would offer a better patient management by allowing low-cost mass screening of AD. This project has also an impact on health and social system. In clinical practice, AD is difficult to diagnose and discriminate from other pathologies. Misdiagnosed patients suffer from unsuitable medical care and have a societal cost. For until their dementia diagnosis, resulting in incremental annual medical costs of approximately $9,500-$14,000 [START_REF] Hunter | Medical costs of Alzheimer's disease misdiagnosis among US Medicare beneficiaries[END_REF].

Our proposal of an automatic prediction method to profile elderly at-risk for AD and to detect AD at early stage will help families and caregivers to undertake the necessary actions before the onset of the disease.

These preventive actions will allow reducing financial costs for healthcare institutions, adapting care programs to each individual, and offering a better follow-up and a personalized management of patients in hospitals.

  from EEG data between SCI, MND, and Mild AD patients was not addressed in the literature. A similar work in the literature in terms of the exploited cohort is that of Liedorp et al.[START_REF] Liedorp | Associations between patterns of EEG abnormalities and diagnosis in a large memory clinic cohort[END_REF], on which abnormalities in different cognitive profiles were investigated based on a visual EEG assessment. Our aim in this project is twofold:

				(v) to discriminate automatically possible AD patients
				from patients who came to the hospital with cognitive
				complaint but with normal age-, gender -and
				education-adjusted performance on cognitive tests i.e.
				SCI patients (AD diagnosis);
				(vi) to discriminate automatically possible AD patients
				from SCI patients and patients with MND or other
				pathologies (differential AD diagnosis).
		most works report studies conducted on small databases, of around 20 persons, containing EEG signals from only healthy subjects and AD patients (a 2-class classification problem); (ii) Second, the proposed methods in the literature tend to have a low specificity, hence poor detection of healthy subjects; (iii) Third, for all these studies, the accuracy of AD	--	Third, it investigates the progression towards dementia by analyzing the evolution of EEG markers, in a transversal way, from SCI, MND to Mild AD, and also in a longitudinal way. Fourth, it assesses the correlation between EEG markers and clinical biomarkers for a better characterization of the cognitive profiles and a better understanding on the severity of the cognitive disorder and the AD diagnosis.
		diagnosis is not evaluated in a differential diagnosis
		context with respect to other pathologies;
		Fourth, at-risk-or preclinical AD individuals have not
		been studied systematically due to the previous lack
		of clear group definitions, and challenges with subtle
		differences to age-matched healthy controls.
		III. MAIN GOALS OF THE PROJECT, EXPECTED INNOVATIONS
	This collaborative research work differs completely from the
	works reported in the literature on AD detection. By contrast,
	this project:
	-	First, addresses the problem of AD prognosis/prediction
		at the "preclinical stage" based on the fusion of EEG
		markers and available clinical data. It differs from
		previous studies in the scientific literature both due to its
		study population (preclinical AD) and the data analysis
		techniques, based on the statistical modeling of the
		multidimensional EEG signal and multimodal data fusion.
		Previously, EEG has been used mainly as a tool for AD
		diagnosis, as widely demonstrated by comparing EEG
		signals of AD dementia patients to those of healthy
		subjects (a classification problem). In contrast, this
		project investigates early AD predictors at the preclinical
		stage, using complex EEG data indicators in amyloid-
		positive and amyloid-negative subjective memory
		complainers (a clustering problem). Our aim is twofold:
		(i) to establish automatically a profiling of elderly at-risk
		of AD;
		(ii) to identify the markers of progression to clinical AD
		in asymptomatic at-risk individuals.

-Second, it investigates EEG markers for an early automatic AD detection at MND and Mild AD stages in a differential diagnosis context. To our knowledge, automatic discrimination

  instance, patients suffering from vascular dementia with prior AD diagnosis use substantially more medical services every year

	Groups	SCI	AD	MND Other
					pathologies
	SCI	81.8% 0%	18.2% 0%
	AD	6.1%	89.9% 2%	2%
	MND	5.2%	6.9%	60.3% 27.6%
	Other	2.5%	5%	47.5% 45%
	pathologies

ACKNOWLEDGMENT

A PhD thesis was funded by the Institut Carnot.

We thank all the staff of the Functional Unit of Charle-Foix hospital for carrying out the EEG and the consultation staff for the patients' care. Myriam Ghezal participated in collecting clinical data. INSIGHT-preAD is funded by generous contributions from the following sponsors: IHU/ICM, PFIZER, AVID, MEMENTO study, Fondation Plan Alzheimer, Institute of Memory and Alzheimer's disease, Paris, France. INSIGHT-preAD study group: Audrain C, Auffret A, Bakardjian H, Baldacci F, Batrancourt B, Benakki I, Benali H, Bertin H, Bertrand A, Boukadida L, Cacciamani F, Causse V, Cavedo E, Cherif Touil S, Chiesa PA, Colliot O, Dalla Barba G, Depaulis M, Dos Santos A, Dubois B, Dubois M, Epelbaum S, Fontaine B, Francisque H, Gagliardi G, Genin A, Genthon R, Glasman P, Gombert F, Habert MO, Hampel H, Hewa H, Houot M, Jungalee N, Kas A, Kilani M, La Corte V, Le Roy F, Lehericy S, Letondor C, Levy M, Lista S, Lowrey M, Ly J, Makiese O, Masetti I, Mendes A, Metzinger C, Michon A, Mochel F, Nait Arab R, Nyasse F, Perrin C, Poirier F, Poisson C, Potier MC, Ratovohery S, Revillon M, Rojkova K, Santos-Andrade K, Schindler R, Servera MC, Seux L, Simon V, Skovronsky D, Thiebaut M, Uspenskaya O, Vlaincu M. INSIGHT-preAD Scientific Committee Members: Dubois B, Hampel H, Bakardjian H, Colliot O, Habert MO, Lamari F, Mochel F, Potier MC, Thiebaut de Schotten M.


D Istrate
email: dan.istrate@utc.fr

C Taramasco

A Fleury

N Houmani

M Hariz

J Boudy

Well-being and -ageing with chronical disease -The BV2 project

Keywords: SoS, wellbeing, well ageing, ADL

The BV2 project aims to propose a monitoring system for wellbeing but also well-aging working on the prevention, detection and monitoring using a System of the Systems (SoS) approach. The project partner already uses the IoT technologies and the BV2 platform will combine the different developed systems. The main originality of the project consists in the development of a virtual platform by combining the existing systems.

I. INTRODUCTION

In today's world, interactive technologies are completely changing our daily lives. They provide an increase support to our everyday needs to make our life safer and more comfortable: from devices that monitor our health (connected devices) to systems that manage assisted care [START_REF]Bringing technology to the people[END_REF].

Healthcare monitoring can be seen as a system of systems (SoS) that includes many operators and needs always larger networks. Research and development in this area is divided into three main levels (Figure 1). The first one is the environment and his influence on human beings. It is a very complex system to handle as security and safety have to be ensured. The second level is the technology that allows perceiving both environment and human beings. The number of sensors and the need to record continuously data (optimization of the recording or ways not to record every time) are one of the research fields. They measure different parameters, send information (either raw or processed data) through the network in order to have a real time analysis. As mentioned in [START_REF] Hata | Human Health Care System of Systems[END_REF], the health management framework is based on a causality model and so is our case of study. Finally, the third level is the human being himself. Based on modern technologies (smart devices), we can collect his health-related sensor data, such as physiological, actimetric (or body moving) and emotional data, which need complex analysis and interpretation. This evaluation helps to devise possible recommendations for people, to detect diseases at an early stage and enhance the quality of life for persons with chronical diseases.

The IoT (Internet of Things) system can be seen as a System of Systems (SoS) that is complex to manage (Figure 2). It is a set of structures that are not necessarily dependent, but manageable together for a better performance and a better life style. We have to take into consideration the different connections levels between the persons, their environments and the connected devices. Sensors will measure data relative to human beings, their environments and the interaction between both of them. Emotions and human response are highly related to hormones and to the environment (work, friends, family, among others).

Figure 1 -SoS components

To analyze emotion, we need to take into account that feelings are personal interpretations. Some people cannot or will not identify what they are feeling. Moreover, even if they do, it still remains a personal point of view. For example, "I might feel stressed" but physiologically "I am just tired", or "I can feel relaxed" but data shows that "I am stressed", etc. We have to consider interactions between different levels, the complexity of data management and decision-making.

II. CONTEXT

The increase of life conditions but also advances in medical treatment has consequence that the population of adults over 60 has presented a global increase, reaching 0.96 billion in 2017, representing an increase of 252% with respect to 1980 presenting a population of 0.38 billion elderly adults. It is expected that by the year 2050, the world population reach around 2.1 billion people over the age of 60 [START_REF]Ageing and health[END_REF]. According to the World Health Organization (WHO), the main health problems that affect this group of persons are chronic diseases and distress situations. Among the chronic diseases, the most frequent are neurodegenerative (Parkinson, Alzheimer and other dementia) and diseases of the circulatory system (hypertension, cardiovascular diseases and metabolic disorders such as type 2 diabetes) [START_REF] Mathers | Projections of global mortality and burden of disease from 2002 to 2030[END_REF][START_REF] Collaboration | Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies[END_REF][START_REF] Bourne | Causes of vision loss worldwide, 1990-2010: a systematic analysis[END_REF][START_REF] Saran | US renal data system 2014 annual data report: Epidemiology of kidney disease in the united states[END_REF]. These diseases also occur in younger people, lying latent for years or decades, and only materializing at later age. Diagnosis of chronic diseases is essential in the medical field as these diseases persist for long time. In Europe, chronic diseases cause at least 86% of deaths (figures of 2005 and expected to continue to grow) [START_REF] Busse | World Health Organization, Tackling chronic disease in Europe: strategies, interventions and challenges[END_REF].

Figure 2 -SoS and IoT approach

In light of demographic change, the care system is facing unprecedented challenges. A larger proportion of older adults live alone in their homes, implying an increased likelihood of suffering distress situations related to home accidents. Falls are especially relevant to patients and health systems because approximately one third of adults older than 65 that live in a community will fall each year, while there are an estimated 3 to 5 falls per 1,000 days of stay in a hospital [START_REF] Oliver | Preventing falls and fall-related injuries in hospitals[END_REF]. In practice, all the industrialized countries are affected by this phenomenon. In Chile, 15% of elderly adults live alone (330 thousand people) [START_REF] Bravo | Las personas mayores que viven solas en chile: 1990-2015[END_REF]. These distress situations have a devastating effect on the quality of life of individuals, and their consequences can result in loss of autonomy. They concern several categories of population from younger to older persons and situations can occur in everyday life at any moment, when working or dealing with home tasks. It encouraged for last decades remote monitoring solutions development for the concerned persons.

The objective of these solutions is the detection and / or prevention of chronic diseases and distress situations based on IoT technologies. However, in general, these systems are limited to the detection and / or prevention of events without having an inter-operable SoS platform allowing the rapid and efficient incorporation of new systems in order to improve the quality of life of the elderly people. The use of IoT technologies currently covers a large number of areas. Internet of Things (IoT) is a term that is more and more used in the computer field [START_REF] Atlam | Fog Computing and the Internet of Things: A Review[END_REF]. It illustrates devices communication through a wireless network, via Bluetooth or depending on pre-established protocols. The smart objects are sensors like the smartwatch, connected lock, thermostat, smart car, connected scale, etc., that are linked and connected in order to record, communicate and exchange data in real-time.

By 2020, we estimate that the number of the connected objects in the world will almost double to reach 50 billion sensors worldwide, compared to 25 billion objects in 2015 [START_REF] Evans | The Internet of Things: How the Next Evolution of the Internet is Changing Everything[END_REF]. As mentioned in [START_REF] Montaigne | Big Data and the Internet of Things Making France a Leader in the Digital Revolution[END_REF], the use of these devices in association with Big Data tools, will contribute to French gross domestic product (GDP) by an increase of 3.6% in 2020 and 7% in 2025. Health technology companies are independently developing IoT solutions, using different platforms and frameworks, implying that devices cannot be integrated with other devices and / or platforms. Using software to enable inter-operability makes it easier for manufacturers to refine or add new features to their products without hardware redesigns. Devices in the field can also be upgraded to fix bugs or include new capabilities, helping to extend the life of IoT devices and reduce management or replacement costs. The standardization of IoT devices for eHealth (related to computer use) and mHealth (related to mobile phone use) of elderly people favors the implementation of systems that can perform their tasks either independently or being part of a system of systems (SoS).

III. STATE OF THE ART

Various investigations offer alternatives to the care of the elderly, focusing their results on the detection and prevention of chronic diseases or distress situations. For example, in [START_REF] Joshi | A fall detection and alert system for an elderly using computer vision and Internet of Things[END_REF][START_REF] Muheidat | Context-Aware, Accurate, and Real Time Fall Detection System for Elderly People[END_REF][START_REF] Jian | A portable fall detection and alerting system based on k-NN algorithm and remote medicine[END_REF] different approaches aiming at detecting and sending fall alerts are presented. However, they are not integrated with other systems. Likewise, in [START_REF] Zhang | Noninvasive Diabetes Mellitus Detection Using Facial Block Color With a Sparse Representation Classifier[END_REF][START_REF] Moreno | Type 2 Diabetes Screening Test by Means of a Pulse Oximeter[END_REF][START_REF] Yu | Room-Temperature Dualmode CMOS Gas-FET Sensor for Diabetes Detection[END_REF] different devices are shown for the detection of diabetes, a chronic disease that affects more than 422 million people worldwide [START_REF]Global Report on Diabetes[END_REF]. Each of these systems does not present a standardization of their communication protocols, making their systems closed for new platforms.

One of the main challenges facing by IoT is the high degree of heterogeneity in the communication of devices, protocols, technologies and hardware. In [START_REF] Vargas | Smart IoT Gateway For Heterogeneous Devices Interoperability[END_REF], the use of a Smart IoT Gateway has been implemented: it acts as a central element communicating with each of the IoT devices through different communication protocols (Wi-Fi, ZigBee, Bluetooth and Ethernet), sending alerts to end users and having a web server that allows internet access to data.

Another way to deal with inter-operability between different devices is based on the use of a middleware such as [START_REF] Pramukantoro | Performance evaluation of IoT middleware for syntactical Interoperability[END_REF], [START_REF] Caione | Exploiting an IoT local middleware for the orchestration of mobile device sensors to detect outdoor and indoor user positioning[END_REF] or [START_REF] Moldovan | An IoT Middleware for Enhanced Security and Privacy: The RERUM Approach[END_REF]. These solutions are focused on networked homes, and are oriented towards protocols such as service discovery protocol and simple service discovery protocol. However, the use of middleware in large areas of IoT could lead to security and scalability issues.

IV. BV2 PROJECT

The BV2 (Wellbeing and aging with chronical disease) project is a Franco-Chili cooperation which has a main objective of the mutual collaboration between the existing research platform in Valparaiso University, University of Technologies of Compiègne and "Institut Mines-Télécom". A mechanism of knowledge transfer between both countries will be created regarding the advances obtained in research related to the care of the elderly. These advances will be unified through a platform based on SoS.

The proposed system based on a SoS approach is designed to data fusion from different sources (environment, embedded sensors, context information) for information extraction about the different systems that compose human body, with the final aim to increase wellbeing of persons with chronical disease but also to well ageing in general. The acceptability criterion will be integrated in a transparent manner.

This project aims at implementing an inexpensive, preventive and effective system in order to ensure a good ambulatory monitoring. We seek to study the impact of using connected objects on chronic diseases, with a main focus on cardiovascular ones. Indeed, 33% of the main cause of death in France can be explained by high cardiovascular disease (according to INSERM-INSEE). It is prevalent among women as well as men. We seek to study the different relationships and correlations existing between the collected data sensors and their impact on cardiovascular diseases, in both prevention and treatment ways. The emotion will be treated according to the HRT [START_REF] Gunnar | Stress et développement précoce du cerveau[END_REF], [START_REF] Ham | Discrimination of multiple stress levels in virtual reality environments using heart rate variability[END_REF]. One of the important emotions that we will treat is stress. It can foster cardiovascular diseases, specifically in the case of a chronic stress. This physiological and psychological reaction develops both anxiety and depression. It increases blood pressure and accentuates cardiovascular disease. The stress we get will be real and not simulated [START_REF] Buckert | Imitation under stress[END_REF]. Smoking, unhealthy eating habit, lack of physical activity and stress accentuate chronic diseases and make healthy people at risk. Firstly, the systems of systems approach will be focused. This theme seeks to integrate already existing platforms developed by the applicant institutions, focused on improving the quality of life of the elderly or young adults through improving the management of healthcare centers. This integration will follow the SoS paradigm, which means a system built from independent systems that are managed separately from the larger system [START_REF] Gideon | Taxonomy of Systems-of-Systems[END_REF]. In this way, Labitec will integrate BV2 via the two mobile applications. The first one, "MyHospital", notifies patients to remind them to attend medical consultations at the hospital. The second platform, "Curare", can be used by the patient to schedule hours at centers for primary healthcare and receive notifications through a mobile application. In addition, the project called "Intelligent system for the management and analysis of the provision of beds in the care network of the public sector", developed by Labitec, will be added. This project consists of a web platform for the management of beds in hospitals, where the healthcare centers adhering to this system implementing inter-operability with HL7 are able to know the number of beds available in the different healthcare centers in order to improve the use of resources. The UTC will integrate the fall detection application based on sound environment analysis and the monitoring of cardiovascular patient using connected objects.

An important research and development activity is dedicated to the interoperability protocols in IoT devices for the BV2 platform, together with the application of the HL7 protocol that allows easy interconnection with healthcare centers.

Secondly, we seek to improve the habits of people to avoid suffering from chronic diseases in older age through the monitoring of biomedical signals; this means to increase the general wellbeing through two themes:

1. Chronic diseases detection and prevention is the first theme. This theme concerns the Information and Communication Technologies helping the healthcare services to offer new possibilities of treatment and follow-up but also to improve their resolution for chronic patients, particularly the cardiac and diabetic patients. The main idea is to propose automatic signal processing of biomedical signals in order to assist the health professionals even though a specialized opinion is not presented, which is the main challenge of the primary healthcare. In this context, Labitec has already developed a low-cost system that detects when an elderly person avoids the use of IoT technologies. This solution, based on the impedance method, does not need to be worn by the user to allow the detection of a micturition event and could be used for early symptom detection of chronic diseases. 2. Chronic diseases monitoring is the second theme.

Labitec has developed a low-cost technological platform to monitor biometrics parameters of domiciliary patient, submitted to a cardiovascular telerehabilitation program, in order to ensure a better quality of life and reinsertion in the familiar and working environments. UTC has started a study on monitoring the emotional state and physical activity of cardiovascular patients. Each patient is equipped with a smart watch able to measure the physical activity, sleep quality and R-R intervals. Each patient is also able to measure the blood pressure with a connected object. The emotions, and more specifically the stress, are estimated from HRV (Heart Rate Variability). One of the important emotions that we will treat is stress. It can foster cardiovascular diseases, specifically in the case of a chronic stress. This physiological and psychological reaction develops both anxiety and depression. It increases blood pressure and accentuates cardiovascular disease. The stress we get will be real and not provoked or simulated [START_REF] Buckert | Imitation under stress[END_REF]. Smoking, unhealthy eating habit, lack of physical activity and stress accentuate chronic diseases and make healthy people at risk.

Thirdly, we seek to improve the quality of life of the elderly persons through remote monitoring; this means well ageing. Its objective is to detect distress situations within the home, as well as to recognize activities of daily life and improve behavior habits. Well ageing is studied through also two themes:

1. Serious Game for prevention and rehabilitation. This theme aims at proposing and implementing various serious games for monitoring cognitive and motor abilities of the elderly at-home. These serious games will consider both actimetric and physiological monitoring of the person. The objective of the use of serious games is: a. to record noninvasively actimetric and physiological information of elderly in their living environment; b. to maintain the cognitive and motor functions of healthy older people by training, in a gamification context, using these data as input to select what training is necessary; c. to detect potential motor frailty and cognitive decline in the elderly at-home; d. to integrate rehabilitation programs, through bio-feedback integrated in these games, in order to regain (within long term training plans) cognitive and motor functions that were impaired by a disease or due to aging.

Activity Daily Life (ADL) monitoring -Application

to the distress situation identification. Through the use of environment monitoring sensors, intelligent algorithms will be implemented to process this information to obtain non-trivial behavior patterns that improve patient comfort. The sound environment is analyzed and some everyday life sounds are recognized in order to ease ADL identification. Some distress expressions are also recognized in order to detect fall or distress situations. A sound analysis system was already developed in the frame of an industrial PhD thesis for Senior@dom start-up; this system consists in a i-vectors based recognition and obtains good recognition rate in current conditions. Another aim is to detect and avoid distress situations at home. In this way, Labitec have implemented a fall-detection system applied to very low-resolution infrared sensors. These sensors are equipped with an array of 16x2 pixels, which assures that the user's privacy is not violated while the device is being used. The system is designed to be employed in homes of older adults that live alone in controlled environments. To process the data from the sensors, recurrent neural networks (RNN) are used, in particular Bi-LSTM.

V. CONCLUSIONS AND PERSPECTIVES

The BV2 project has started at the beginning of 2019 and aims to propose a common, interoperable platform for well being and well ageing based on the System of Systems (SoS) approach. Each project partner has already developed different approach for home monitoring through different IoT and environmental sensor but the originality is the combination of these systems in a virtual common platform. This virtual platform will allow the possibility to design and test new and fully interoperable systems combination concepts even if in different country.

ACKNOWLEDGMENTS

The BV2 project is funded by ECOS SUD program.


Philippe Ménini
email: menini@laas.fr

H Chalabi

E Scheid

F Parret

C Tropis

A Martinez

V Conedera

L Salvagnac

S Assie-Souleille

B Chaudret

A Maisonnat

K Soulantica

P Fau

K Aguir

Biographie Of

Development of a New Micromachined Metal Oxide Gas Sensor: Application to Hazardous Gas Detection for Automotive Air Quality Control

Keywords: analogue IC for Microsystems, instrumentation, electrical modelling Micromachined gas sensor, Temperature cycling, Discriminate variables

This article deals with a new generation of semiconducting gas sensor based on a microhotplate realized on a silicon nitride membrane associated with an optimized temperature profile (T.P.) applied on heating resistor. Firstly, the technological process has been modified in order to enhance as a priority the long term stability and to allow the sensor working at higher temperature (>450°C). Secondly, a new method of data treatment is also investigated from normalized transient response of the sensor. The main goal of this new method is to identify different gas mixtures and quantify the target gas concentration. In this article, this new operating mode applied to a single Pt-doped SnO 2 gas sensor is presented to discriminate CO-concentrations in synthetic air in presence of interfering gases as NO 2 and C 3 H 8 independently of relative humidity rate. One of applications is the air quality control in automotive cabin.

Introduction

Presently, most of metal oxide gas sensors have poor selectivity and instability because of their own electrical behaviour. These sensors are generally realized even on ceramic substrates with a platinum resistor as heater on the backside or on microhotplate with a silicon membrane and a polysilicon resistor as heater. The first kind of sensor is well known and easy to do technologically but provides significant thermal inertia. On the opposite, the microhotplate on membrane yields to very low thermal inertia (30ms for temperature growing from ambient to 450°C). Moreover, it is compatible with low cost silicon technology. The problem is the well known drift of the polysilicon resistance when it is run right across an important level of current density (few hundreds kilo amps per square inches). From the development of new thin films and nanostructured sensitive layers that improve significantly the surface/volume ratio, the sensitivity of this new sensors have been multiplied by a factor 5 at least [1]. In an other hand, even if the selectivity can be theoretically improved by adding dopants (Pt, Pd,…) in the sensing material [START_REF] Maisonnat | New generation of micro machined silicon gas sensors[END_REF] or filters on top of it, the experimental figures show mitigated results in a standard operating mode (i.e. at constant temperature). Finally, their instabilities are now a day not solved. Previous studies have demonstrated the interest to use micromachined gas sensors in temperature modulation to improve performances in term of gas selectivity and sensitivity and to reduce significantly the influence of relative humidity rate [START_REF] Schweizer-Berberich | Pulsed mode of operation and artificial neural network evaluation for improving the CO selectivity of SnO 2 gas sensor[END_REF][START_REF] Guerrero | Method of C 2 H 4 detection in humid atmospheres using nanoparticular SnO 2 gas sensor[END_REF]. In order to improve significantly these 3 capital sensor features, we work in two parallel ways: Firstly, we try to design and to develop technologically a new micro-hotplate with reliable thermo-electrical behaviour [5]. Secondly, we investigate a new operating mode that consists in short temperature cycles to discriminate different target gases [START_REF] Parret | optimised dynamic temperature operating mode specifically dedicated to micromachined Sno2 gas sensors[END_REF]. Associated to a suitable data processing, we are able to quantify for example CO concentrations in a mixture with a single Pt-doped SnO 2 sensor. This work, concerning the data processing, has been initiated in the frame of the "Nanosensoflex" European project (G5RD- CT-2002-00722 ;2002[START_REF] Gillet | Grain size effect in sputtered WO3 thin films on the sensitivity to O3[END_REF] in collaboration with 9 partners from which Mics (Microchemical Systems, CH), University of Saarland (D), Nanosense (F) and CRF (Fiat Research Center, I).

Sensor description

The sensors used in this study consist in a microhotplate platform and metal oxide sensing layers. The first generation of microhotplate architecture was initially developed conjointly by LAAS and Motorola and presently exploited by Microchemical Sensors S.A (figure 1). A SiO x N y membrane of about 2 µm of thickness supports an n-type polysilicon heater of 600µm x 430µm. Geometry and sizes were optimized to achieve good thermo-mechanical reliability and good homogeneity of temperature on the active area. This first generation with polysilicon heater can reach temperatures of about 450 o C with power consumption lower than 100mW. An important result from the European project studies lies in the comparison with simulation, which, in spite of a general agreement concerning the design dependence, indicates some discrepancies concerning the temperature distribution. The most innovative result in this study is the fact that these discrepancies are dues to the aging of the resistance. Infra-red observations have revealed that this aging, which is included in the fabrication process, leads to higher non homogeneities of temperature in the hottest parts. This also indicates that the aging at high temperature has two opposite effects: an increase of the global resistance, and a decrease of the resistance in the hottest part of the resistor. This would have an impact on a possible optimization of the aging procedure. Moreover, the drift measured during the first 4 months (of about 10%Rh/month) induces a decrease of the power (i.e a decrease of temperature -50°C), then a decrease of sensitivity: about -20% of the sensor CO-sensitivity (S = 100*(Rgas-Rair)/Rair) (figure 2). This drift could be explained partially by the polysilicon drift, but also by other phenomena like the sensitive layer ageing electrically (modification of the contact quality, of the charge mobility, oxidation rate, pollutants in the sensitive layer), morphologically (but no particle size variation has been observed at TEM microscopy) or finally thermo-mechanically (cracks,…). Finally, the dispersion observed on a wafer can reach 30% on the heater resistances that implies to sort out them rigorously.

Recent developments have been made by LAAS-CNRS and L2MP, to conceive a new generation of microhotplate which integrates platinum heater (figure 3). The main interest consists in replacing polysilicon heater by a platinum one with potential longer lifetime. The membrane is presently realized with a SiO 2 -SiN x bi-layer (respectively 1.4µm and 0.6µm of thickness) with x≈1.2 in order to obtain a very low residual stress (< 100MPa) and a good thermo mechanical behavior. Different geometries of heater resistors have been designed, simulated thermo-electrically with ANSYS software and realized to compare the power efficiency (yield) and the lower temperature gradient as possible in the active layer region (∆T<±20°C on ±150µm around the center of the membrane). Then, a 0.5µm of insulated layer (SiO 2 deposited by PECVD: Plasma Enhancement Chemical Vapor Deposition). Then, interdigitated electrodes are realized with Ti/Pt metallization obtained by electron beam evaporation with a specific annealing to minimize residual stress of Pt. Finally, the membrane is elaborated at the end of the fabrication process by DRIE (Deep Reactive Ion Etching) of the silicon on the backside which allows vertical walls and with an etching rate of about 3.2µm/mn. On the other side, the sensitive layer could be either a "standard" WO 3 metal-oxide deposited by reactive RF magnetron sputtering [START_REF] Gillet | Grain size effect in sputtered WO3 thin films on the sensitivity to O3[END_REF] (developed by L2MP) or a crystalline SnO 2 nanomaterial synthesized by the decomposition and oxidation of a tin based organometallic precursor ([Sn(Nme 2 ) 2 ] 2 ), with a mean grain size near 15 nm of diameter (developed by LCC-CNRS). Doping of this second one is achieved by decomposing an organometallic precursor under dihydrogen at the surface of the tin/tin oxide preformed particles. Upon heating in situ on the platform the tin material is transformed into SnO 2 , whereas the doping agents are oxidized into PdO or PtO nanocrystals which mostly remain at the surface of tin oxide. This nanomaterial is then deposited using a microinjection technique over the platinum electrodes placed in the homogeneous temperature region of the heater. This heater permits the full oxidation into SnO 2 with a controlled temperature cycle from ambient to 500°C. Mics has already developed this technique industrially to dispense the colloid very quickly (few thousand sensors a day) and very precisely (± few microns)(see the SnO 2 drop on figure 1).

Pt Heater Si

Si substrate

Cold part Cold part Warm part

Experimental details

In collaboration with LCC-CNRS, we developed a semi-automatic test bench that allows characterization of 15 sensors in parallel under controlled atmosphere. The experimental setup consists of a gas delivery system, an exposure glass vessel of one liter (padding time of 2min) and an electronic circuit for resistance determination through voltage measurements.

Tests have been performed under CO, NO 2 and C 3 H 8 mixtures. With a constant flow rate, the relative humidity rate has been fixed from 30% to 70% to evaluate its influence. a) Sensor operating mode Most of semiconducting gas sensors work at constant temperature and some others use a pulsed working temperature. In this second mode, the data are generally collected up to 15 or 20 seconds after the end of the pulse to release the response from the humidity influence. More recent articles [START_REF] Marc | Schütze Andreas -A new method for fast identification of gases and gas mixtures after sensor power up[END_REF][START_REF] Ruiz | Analyses of the ammonia response of integrated gas sensors working in pulsed mode, EurosensorsXIX[END_REF] use a "rapid" temperature modulation to evaluate transient response but with a too slow sample acquisition time to observe all transient chemisorption's effects. Our main objective was to conceive a test profile with flexible parameters in order to find quickly the optimal temperature variations that allow efficient discrimination (Fig. 4). For that, we make varying the temperature between ambient and 500°C (max) corresponding to 3.4V on heater. In this test profile, the flexible parameters are temperature variation, frequency and temperature offset (base line). We cycled this profile and realise test under different ambient atmosphere (relative humidity level (%RH), gas concentrations, mixtures …). Typical response curve of the pulsed sensors when exposed to constant CO concentration (200 ppm) is shown on figure 5. These curves have been obtained reproducibly at three different tests successively. From this full temperature profile, we can define an optimized operating mode dedicated to one application (one target gas in a mixture). The figure 5b shows the resistance variations at different temperature steps. It can be seen that the NO 2 can be discriminated at low level of temperature. This new optimized temperature profile 1 (figure 6), with a total duration of three seconds, is based on a first step of high temperature to assure the sensor stability and reproducibility associated with three specific temperature levels allocated to the gas measurement. After these 4 steps the heater is switched off to minimize the mean power consumption lower than 40mW and to allow calculation time lower than 10s.

b) Data acquisition

In order to get more accurate insight on the transient responses of the sensitive layer on each temperature variation, each resistance value R i is normalized according to the following equation:

(1)

f f i n R R R R / ) ( - =
1 Elaborated for CO-detection in collaboration with university of Saarland (D) during the Nanosensoflex project.

Where R n is the normalized value for the resistance R i measured at time t and R f is the last value measured on each step of temperature.

c) Example of experimental results

Typical conductance transient responses of the Pt-doped SnO 2 sensor exposed to stabilized gas mixtures yield to different response shapes which are dependent to ambient mixture and in a lower range to gas concentrations (figure 7). These first results have been obtained under different gases separately (without mixture), then with mixtures of CO, C 3 H 8 and NO 2 , and with 50% of relative humidity. These results demonstrate the interest to observe the shape of the sensing layer response which depends of the temperature variations. The classification of the different classes has been realized by Linear Discriminate Analysis (LDA) (figure 8).

Conclusion

A new generation of semi-conducting gas sensor has been designed and realised. It consists in a new microhotplate which uses platinum resistor as heater, a 2µm-thickness SiO2+SiNx membrane on silicon etched by DRIE, SiO2 by PECVD as insulated layer and platinum contact. This die includes also a platinum resistance as ambient temperature sensor. The sensing layer is either a nanostructured SnO2 deposited by drop deposition or a 50 nmthickness of WO3 deposited by RF magnetron sputtering. First characterisation results have been obtained with nanoparticular SnO 2 sensor. We demonstrated that the sensor could be selective in a mixture and independent of relative humidity when it works in very fast temperature modulation. These first results show that the shape of the sensing layer response on a temperature variation is significantly affected by a gas or gas mixture. These shapes, formed with peak and slope, can be treated and exploited by using efficient variables. The feasibility of CO detection has been shown with a specific operating mode. This new profile with a total duration of 3 seconds includes one step for stabilization and 3 steps for discrimination. Three different CO concentrations (40ppm, 80 ppm and 120ppm) have been discriminated in a mixture of two interfering gasses (1.8ppm of N02 and 200ppm of C3H8). The extraction of these efficient variables and a mathematical analysis like FDA could be implemented in an appropriate integrated circuit (microcontroller, DSP,…) to realize a prototype of selectively gas detector for automotive application.

D

  ie lec tric me mbrane S ens ing lay er e le ctrode He ating res istor H eat er elec trode Na nopartic ula r SnO 2 sen sing la ye r D ie lec tric me mbrane S ens ing lay er e le ctrode He ating res istor H eat er elec trode Na nopartic ula r SnO 2 sen sing la ye r

Figure 1 :

 1 Figure 1: Top of view of SnO 2 gas sensor developed by MICS in the frame of Nanosensoflex Project.

Figure 2 :

 2 Figure 2 : a) Power efficiency degradation vs. time ; b) Isotherm CO-sensitivity vs. working temperature

Figure 3 :

 3 Figure 3: New generation of Microhotplate developed by LAAS-CNRS and L2MP: a) cross section scheme; b) top of view of the microhotplate.

Figure 4 :

 4 Figure 4: full working temperature profile: temperatures steps through voltage steps on the heater

Figure 5

 5 Figure 5. a) Example of sensor responses: 3 sequences referenced at the same time to show the reproducibility ; b) ∆Rs evolution versus voltage variations for 4%Pt-doped SnO2 sensor

Figure 6 :

 6 Figure 6 : Optimized working Temperature Profile for CO-detection with a single Pt-doped SnO2 gas sensor.

Figure 7 :

 7 Figure 7: Normalized response curves at two different temperature steps : a) from 300°C to 400°C ; b) from 110°C to 240°C

Figure 8 :

 8 Figure 8: LDA projection for discrimination the 5 classes (with two r.h.values).