Sommeil et électroencéphalographie, preprocessing et analyses d'un motif EEG de sommeil.

Preprocessing and analyses of a specific sleep EEG pattern. Automatic detection methods, self-adjustable to individuals characteristics, were developed in order to remove artefacts from EEG sleep recordings, detect sleep spindles and analyse NREM power spectrum. Methods are described, assessed and discussed

surveillance non invasive du sommeil

Surveillance non invasive du sommei

FASST - fMRI Artefact rejection and Sleep Scoring Toolbox

"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial support of the Fonds de la Recherche Scienti que-FNRS, the Queen Elizabeth's funding, and the University of Li ege. On Dr. Pierre Maquet's impulse we started writing these tools to analyze our sleep EEG-fMRI data and tackle four crucial issues: * Continuous M/EEG. Long multi-channel recording of M/EEG data can be enormous. These data are cumbersome to handle as it usually involves displaying, exploring, comparing, chunking, appending data sets, etc. * EEG-fMRI. When recording EEG and fMRI data simultaneously, the EEG signal acquired contains, on top of the usual neural and ocular activity, artefacts induced by the gradient switching and high static eld of an MR scanner. The rejection of theses artefacts is not easy especially when dealing with brain spontaneous activity. * Scoring M/EEG. Reviewing and scoring continuous M/EEG recordings, such as is common with sleep recordings, is a tedious task as the scorer has to manually browse through the entire data set and give a \score" to each time-window displayed. * Waves detection. Continuous and triggerless recordings of M/EEG data show specifi c wave patterns, characteristic of the subject's state (e.g., sleep spindles or slow waves). Their automatic detection is thus important to assess those states.In order to work properly, FASST needs to have those 2 softwares installed: - a recent version of Matlab , we used version 7.5 (R2007b) to develop FASST. Any later Matlab version should work, in theory. - the latest version of the "Statistical Parametric Mapping Software", i.e. SPM8. You can download it from: http://www.fil.ion.ucl.ac.uk/spm/software/. Install it in a suitable directory, for example C:\SPM8\, and make sure that this directory is on the Matlab pat

Automatic artifact detection for whole-night polysomnographic sleep recordings

Detecting of bad channels and artifacts for whole-night polysomnographic recordings is very time consuming and tedious. We therefore developed an automatic procedure to automatize this job

FASST - fMRI Artefact rejection and Sleep Scoring Toolbox

"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial support of the Fonds de la Recherche Scienti que-FNRS, the Queen Elizabeth's funding, and the University of Li ege. On Dr. Pierre Maquet's impulse we started writing these tools to analyze our sleep EEG-fMRI data and tackle four crucial issues: * Continuous M/EEG. Long multi-channel recording of M/EEG data can be enormous. These data are cumbersome to handle as it usually involves displaying, exploring, comparing, chunking, appending data sets, etc. * EEG-fMRI. When recording EEG and fMRI data simultaneously, the EEG signal acquired contains, on top of the usual neural and ocular activity, artefacts induced by the gradient switching and high static eld of an MR scanner. The rejection of theses artefacts is not easy especially when dealing with brain spontaneous activity. * Scoring M/EEG. Reviewing and scoring continuous M/EEG recordings, such as is common with sleep recordings, is a tedious task as the scorer has to manually browse through the entire data set and give a \score" to each time-window displayed. * Waves detection. Continuous and triggerless recordings of M/EEG data show specifi c wave patterns, characteristic of the subject's state (e.g., sleep spindles or slow waves). Their automatic detection is thus important to assess those states.In order to work properly, FASST needs to have those 2 softwares installed: - a recent version of Matlab , we used version 7.5 (R2007b) to develop FASST. Any later Matlab version should work, in theory. - the latest version of the "Statistical Parametric Mapping Software", i.e. SPM8. You can download it from: http://www.fil.ion.ucl.ac.uk/spm/software/. Install it in a suitable directory, for example C:\SPM8\, and make sure that this directory is on the Matlab pat

New spectral analysis method to identify trait-like features in NREM sleep power spectra

Introduction: Variability among individuals in sleep/wake biology and behaviour is pervasive. Sleep/wake-related variability involves individual trait-like features (ITLF). In order to study ITLF, we developed an advanced spectral analysis method (ASAM) to compare in a systematic and reproducible way 3 features of NREM power spectra (PS). As sleep spindles (SS) have been reported several times to be an important ITLF, we focused on a broad range of sigma activity (10 − 18Hz). Methods: The ASAM is composed of three main steps. First, EEG sleep recording is preprocessed and the NREM PS is computed for each channel of interest. Second, three characteristic features from the NREM PS related to sigma peak activity are extracted: magnitude, peak location and standard deviation. Third, the intra-individual stability and the inter-individual variability of these features, as well as the influence of sleep perturbations, are statistically tested using the Intra-Class Correlation (ICC) coefficient and the Wald Z-test. The performance of the ASAM was assessed using different polysomnography-derived sleep recordings from 16 healthy young male subjects (18-30 years). For each subject, these recordings were acquired in 5 different “sleep contexts”: the acquisition was varied from 4h to 12h and was performed at different circadian phases. Results: All three features of sigma activity (magnitude, peak location and its standard deviation) were recognized as important ITLFs (ICC = 0.74, 0.94 and 0.63 respectively). Furthermore, the inter-individual variability of these three features was significant different from zero (Wald Z-test ps < 0.05). Conclusion: Three aspects of NREM PS sigma activity are recognized as important ITLFs. It is also showed that even though these features changed according to sleep context, they remained specific to the individuals. Establishing the trait-specific nature of variability in sleep/wake parameters could elucidate genetic mechanisms

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Arousals during sleep are transient accelerations of the EEG signal typically detected by visual inspection of the sleep recording. Such visual identification is a time-consuming, subjective process that prevents comparability across scorers, studies and research centres. We developed an algorithm, which automatically detects arousals in whole-night EEG recordings, based on time and frequency analysis with adapted thresholds derived from individual data. We performed automatic arousals detection over 35 sleep recordings of young (µ=24.07±3, N=18) and older (µ=61.38±6, N=17) healthy individuals, and compared it against human raters (HR) detection from two research centres. We assessed performance of the automatic algorithm using generalized linear mixed models with Cohen’s kappa as dependent variable. Performance of automatic detection was compared to a gold standard, composed of either all arousals found by any of the HR (inclusive detection – ID) or only those common to both HR (conservative detection – CD). Comparison between human scorers revealed a high variability in the number of arousals detected (µ=71±32 vs 111±50). Although many more arousals were automatically detected (µ=200 ± 43), agreement of automatic detection against human detection was high, as reflected by very large Cohen’s kappa values (κ=.93 for ID, .94 for CD). Importantly, automatic detection was correlated to human detection (r=.38, p=.025 for CD). Algorithm performance was not significantly influenced by sleep stage (p=.74 for ID; p=.97 for CD), age (p=.12 for ID; p=.91 for CD) or sex (p=.10 for ID; p=.21 for CD). We further found that relative power in the theta and alpha bands were, respectively, higher and lower (p<.0001) for arousals that were only detected by the algorithm, arguably making them less obvious for the human eye. Our results show that the automated algorithm is performing at least equally as well as HR. While the automatic method detects most of HR events, it finds many more events that bear the characteristics of AASM arousals, but are missed by visual inspection of the EEG. This is seen for other micro-events detectors such as spindle detectors. In conclusion, our algorithm a reliable tool for automatic detection of arousals