Modulation of the Sympatho-Vagal Balance during Sleep: Frequency Domain Study of Heart Rate Variability and Respiration

Sleep is a complex state characterized by important changes in the autonomic modulation of the cardiovascular activity. Heart rate variability (HRV) greatly changes during different sleep stages, showing a predominant parasympathetic drive to the heart during non-rapid eye movement (NREM) sleep and an increased sympathetic activity during rapid eye movement (REM) sleep. Respiration undergoes important modifications as well, becoming deeper and more regular with deep sleep and shallower and more frequent during REM sleep. The aim of the present study is to assess both autonomic cardiac regulation and cardiopulmonary coupling variations during different sleep stages in healthy subjects, using spectral and cross-spectral analysis of the HRV and respiration signals. Polysomnographic sleep recordings were performed in 11 healthy women and the HRV signal and the respiration signal were obtained. The spectral and cross-spectral parameters of the HRV signal and of the respiration signal were computed at low frequency and at breathing frequency (high frequency, HF) during different sleep stages. Results attested a sympatho-vagal balance shift toward parasympathetic modulation during NREM sleep and toward sympathetic modulation during REM sleep. Spectral analysis of the HRV signal and of the respiration signal indicated a higher respiration regularity during deep sleep, and a higher parasympathetic drive was also confirmed by an increase in the coherence between the HRV and the respiration signal in the HF band during NREM sleep. Our findings about sleep stage-dependent variations in the HRV signal and in the respiratory activity are in line with previous evidences and confirm spectral analysis of the HRV and the respiration signal to be a suitable tool for investigating cardiac autonomic modulation and cardio-respiratory coupling during sleep

Fusion d'avis d'experts et caractérisation de l'expertise. Application à la détection de transitoires dans les signaux physiologiques

L'élaboration d'une règle de décision à partir d'une base d'apprentissage suppose en général que l'étiquetage des données utilisées n'est pas entaché d'erreur. Cette situation idéale n'est cependant pas toujours réaliste, en particulier lorsque les phénomènes à détecter sont complexes. L'objectif de ces travaux est de proposer une méthode pour la fusion d'expertises divergentes qui autorise une analyse a posteriori de la pertinence de chacun des experts. La méthode proposée repose sur la maximisation de l'information mutuelle normalisée entre les observations et une fonctionnelle des avis émis par les experts. La méthode a été appliquée avec succès à la détection de transitoires dans les signaux physiologiques du sommeil

Mixture of experts for automated detection of spontaneous phasic arousals in sleep signals

International audienc

Mixture of experts for automated detection of spontaneous phasic arousals in sleep signals

International audienc

Detection of Levodopa Induced Dyskinesia in Parkinson's Disease Patients Based on Activity Classification

International audienceIn this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88

Feature Selection for Activity Classification and Dyskinesia Detection in Parkinson's Disease Patients

International audienceRecent advances in wearable sensing technologies have favored the search for reliable and objective methods of estimating motor symptoms and complications of Parkinson's disease (PD). In this paper, we present a complete system of motor assessment composed of Shimmer3 inertial measurement modules aimed to classify a series of daily life activities performed by PD patients and detect the occurrence of Levodopa Induced Dyskinesia (LID). Feature selection methods are implemented on datasets collected from nine healthy individuals and 2 PD patients in order to determine the most relevant module positions with respect to activity classification and detection of LID. Classifying activities resulted in an overall accuracy of 88.05% in healthy individuals and 85.87% in PD patients, while detection of dyskinesia yielded 83.89%. The lowered performance is likely to be caused by the difficulty of classifying PD patients' activities due to presence of motor dysfunction

Activity Recognition Using Complex Network Analysis

International audienceIn this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body

Activity Recognition Using Multiple Inertial Measurement Units

International audienceObjectives: This paper addresses the design of an ambulatory monitoring system based on a set of wearable, wireless inertial measurementunits able to perform activity recognition for healthy individuals and Parkinson’s disease patients, as well as analyze and assess the severity oflevodopa induced dyskinesia.Material and methods: The monitoring system is composed of six Shimmer3 modules placed at different positions of the individual’s body.Both healthy individuals and one patient performed a protocol of simple daily life activities while wearing the Shimmer3 modules. As an initialstep, validity of the monitoring system in identifying healthy individuals’ activities is assessed. Data corresponding to the activities was separatedand features in both time and frequency domains were extracted. Multiple factor analysis was used to evaluate and infer the relationships betweenthe different module positions. A method of feature selection was implemented to determine the most important features, positions and sensorsincluded in the different modules. The classification of activities was done using a KNN classifier.Results: Promising results were obtained in classifying the activities of healthy individuals, with a global accuracy of 77.6%. However, certainadaptation is required for the application on Parkinson’s disease patients.Conclusion: While activity recognition for healthy individuals using this system was successful, further evaluation of the contribution of eachmodule needs to be done in order to determine optimal module positions. To validate the obtained results on Parkinson’s disease patients, a largerstudy based on more patient acquisitions is envisioned

Modulation of the sympatho-vagal balance during sleep: frequency domain study of heart rate variability and respiration

Sleep is a complex state characterized by important changes in the autonomic modulation of the cardiovascular activity. Heart rate variability (H RV) greatly changes during different sleep stages, showing a predominant parasympathetic drive to the heart during non-rapid eye movement (NREM) sleep and an increased sympathetic activity during rapid eye movement (REM) sleep. Respiration undergoes important modifications as well, becoming deeper and more regular with deep sleep and shallower and more frequent during REM sleep. The aim of the present study is to assess both autonomic cardiac regulation and cardiopulmonary coupling variations during different sleep stages in healthy subjects, using spectral and cross-spectral analysis of the HRV and respiration signals. Polysomnographic sleep recordings were performed in 11 healthy women and the HRV signal and the respiration signal were obtained. The spectral and cross-spectral parameters of the HRV signal and of the respiration signal were computed at low frequency and at breathing frequency (high frequency, HF) during different sleep stages. Results attested a sympatho-vagal balance shift toward parasympathetic modulation during NREM sleep and toward sympathetic modulation during REM sleep. Spectral analysis of the HRV signal and of the respiration signal indicated a higher respiration regularity during deep sleep, and a higher parasympathetic drive was also confirmed by an increase in the coherence between the H RV and the respiration signal in the HF band during NREM sleep. Our findings about sleep stage-dependent variations in the HRV signal and in the respiratory activity are in line with previous evidences and confirm spectral analysis of the HRV and the respiration signal to be a suitable tool for investigating cardiac autonomic modulation and cardio-respiratory coupling during sleep