Millions of people suffer from sleep disorders
Millions of people suffer from sleep disorders (Apnea, Insomnia, Hypersomnia…) in Europe, in the US, all over the world. Sleep disorders increase with age and overweight.
Diagnosing sleep disorders is difficult. Sleep exams need various data to be analyzed (breathing, saturation, electrophysiology) which require expertise and time.
The automated algorithm ASEEGA® was validated first on healthy subjects, then on patients: Sleep Apnea, Insomnia, Hypersomnia, Narcolepsy, Mild Cognitive Impairments so far. ASEEGA was proven to be as reliable as an expert scorer.
for the analysis of sleep
Physip has developed ASEEGA®, an automated analysis algorithm, which provides :
- Automated sleep staging
- Calculation of sleep parameters
- Micro arousal detection
Automated analysis is a way to
- Reduce time and cost of data analysis
- Allow more sleep exams to be performed and analyzed with no other limit than the processing capacity
Frequently Asked Questions
Is the technology limited to nights recorded in a sleep laboratory?
ASEEGA® can analyse nights recorded both in a laboratory and in an outpatient setting.
How can I convert my traces to the edf format?
Most of the current polygraphs and polysomnographs provide the option to export recording files in the edf format. Please get in touch with us or with your usual partner if you need help to perform this operation.
What is special about your analysis method?
All our analysis methods share the same characteristics: they are entirely automatic, based only on the EEG and on a single EEG lead, preferably located at CzPz. Therefore, they achieve a unique compromise between performance and practicality.
What format are the results provided in?
Pdf reports are available. Results are provided as Excel reports, Matlab structure or XML files.
📖 Berthomier C. & Brandewinder M., EOG-based auto-staging: less is more, Sleep Breath, 2015
This issue of Sleep and Breathing presents the validation results of a new automated wake/sleep staging method based on EOG activity, developed by Jussi Virkkala from the Finnish Institute of Occupational Health. Classically, the automated method is compared to visual analysis, on an epoch by epoch basis. It reaches a level of global concordance of 88 % with a Kappa of 0.57. In other words, on the 248,696 epochs of the validation dataset, 212,138 were scored correctly in wake/sleep, that is as the human expert did it, and on 36,558 epochs, the two scorings differ.
> Full text: EOG-based auto-staging: less is more
📖 Berthomier C. & Brandewinder M., Sleep scoring: man vs. machine ?, Sleep Breath., 2013
Berthomier C, Brandewinder M, Sleep scoring: man vs. machine ?, Sleep Breath. 17 (2):461-462, 2013
The automated analysis of sleep has grown in interest in the past decade. Advances in computing have brought the needed intensive calculations within reach; while simultaneously, there is an increasing demand for sleep diagnosis and analysis. The prevalence of sleep troubles is high, and the awareness of their consequences is spreading among patients, health authorities, and clinicians. This awareness is directing more and more patients to sleep centers. The upward trend in demand for sleep evaluations concerns not only sleep specialists. Sleep appears to be an extremely promising territory for other fields, such as cardiology and nutrition for example. Needs exceed capacities by this far. Data analysis has been identified as one of the bottlenecks in the sleep evaluation process, making clear the importance of developing tools to facilitate analysis. These developments have an impact that is medical, as well as economical and social.
> Full text: Sleep scoring: man vs. machine ?
📖 Berthomier C., et al., Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring, bioRxiv 2019
C. Berthomier, V. Muto, C. Schmidt, G. Vandewalle, M. Jaspar, J. Devillers, G. Gaggioni, S. Chellappa, C. Meyer, C. Phillips, E. Salmon, P. Berthomier, J. Prado, O. Benoit, M. Brandewinder, J. Mattout, P. Maquet, Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring, BioRxiv doi: https://doi.org/10.1101/576090
Study Objectives: New challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the inter- and intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined. Methods: A first dataset (DS1, 4 recordings) scored by 6 experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to investigate intra-expert variability. Percentage agreements and Conger′s kappa were derived from epoch-by-epoch comparisons on pairwise, consensus and majority scorings. Results: On DS1 the number of epochs of agreement decreased when the number of expert increased, in both majority and consensus scoring, where agreement ranged from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0.81 to 0.79. Agreement between expert consensus and autoscoring was 93%. On DS2 intra-expert variability was evidenced by the kappa systematic decrease between autoscoring and each single expert between datasets (0.75 to 0.70). Conclusions: Visual scoring induces inter- and intra-expert variability, which is difficult to address especially in big data studies. When proven to be reliable and if perfectly reproducible, autoscoring methods can cope with intra-scorer variability making them a sensible option when dealing with large datasets.
📃 Chylinski D., et al., Sleep fragmentation is associated with brain tau but not amyloid-β burden in healthy older adults, Front. Neurosci. Conference Abstract 2019
Chylinski D, Rudzik F, Coppieters ‘T Wallant D, Van Egroo M, Muto V, Narbutas J, Villar González P, Besson G, Lambot E, Laloux S, Hagelstein C, Ghaemmaghami P, Degueldre C, Berthomier C, Bethomier P, Brandewinder M, Schmidt C, Maquet P, Salmon E, Phillips C, Bahri M, Bastin C, Collette F and Vandewalle G (2019). Sleep fragmentation is associated with brain tau but not amyloid-β burden in healthy older adults. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 —
Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00054
Belgian Brain Congress 2018
📖 Taillard J. et al., Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population, Front. Neurol. 2019
Jacques Taillard, Patricia Sagaspe, Christian Berthomier, Marie Brandewinder, Hélène Amieva, Jean-François Dartigues, Muriel Rainfray, Sandrine Harston, Jean-Arthur Micoulaud-Franchi and Pierre Philip, Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population, Front. Neurol., 13 March 2019 | https://doi.org/10.3389/fneur.2019.00197
Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer’s disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults.
Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night.
Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05).
Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.
📖 Ghaemmaghami P., et al. The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018
Ghaemmaghami P, Muto V, Jaspar M, Meyer C, Elansary M, VanEgroo M, Berthomier C, Lambot E, Brandewinder M, Luxen A, Degueldre C, Salmon E, Archer SN, Phillips C, Dijk D, Posthuma D, Van Someren E, Collette F, Georges M, Maquet P and Vandewalle G (2018). The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00069 Introduction. Identifying risk factors for insomnia in individuals that are likely to develop insomnia is needed to develop prevention strategies. Novel genetic tools using results of large case-control genome wide association studies (GWAS) allow to predict the liability for complex diseases based on full-genome common genetic variations. Here, we applied such tools to assess the link between the genetic liability of developing insomnia and sleep phenotypes in young and healthy adults not reporting any sleep complaint. Methods. Electroencephalography was recording during 8h baseline sleep in 360 healthy young male volunteers with normal sleep (aged 22.09 y ± 2.71). Sleep architecture, the percentage and latency of each sleep stage, and the total number, hourly rate and mean duration of awakenings were extracted from automatic sleep scoring (Aseega, Physip). Blood samples were collected in all participants to assess common Single Nucleotide Polymorphisms (SNPs) over the entire genome. Individual liability for insomnia was computed based on whole genome SNPs using the summary-statistics of a case-control GWAS seeking for genetic determinants of insomnia (Hammerschlag et al. Nat Genet 2017;49:1584–1592, https://ctg.cncr.nl/software/summary_statistics). Results. Generalized linear mixed model reveal significant associations of one’s genetic risk score for insomnia with the percentage of sleep stage 2 (r = 0.13, p <0.05) and the percentage of sleep stage 3 (r = -0.12, p < 0.05). These results suggest that higher liability for insomnia is associated with lower percentage of slow wave sleep while it is associated with higher percentage of lighter sleep. The results remain significant after adjusting for age. Conclusion. These results show that individual genetic liability for insomnia is linked to sleep lower amount of what is considered most important to dissipate sleep need (i.e. slow wave sleep) in young and healthy individuals. These findings could help identifying novel prevention targets for insomnia. > Full text: The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals.
📃 Escourrou P. J., et al., Respiratory events during stable wake in sleep apnea patients: occurrence and mechanism, European Respiratory Journal 2018
P. J. Escourrou, M. Brandewinder, C. Berthomier, P. Berthomier, G. Baffet, Z. Balekji, N. Puisais, G. Roisman, Respiratory events during stable wake in sleep apnea patients: occurrence and mechanism., European Respiratory Journal 2018 52: PA2509; DOI: 10.1183/13993003.congress-2018.PA2509
📃 Muto V. et al., Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis, Sleep, 2018
V. Muto, C. Berthomier, C. Schmidt, G. Vandewalle, M. Jaspar, J. Devillers, S. Chellappa, C. Meyer, C. Phillips, P. Berthomier, J. Prado, O. Benoit, M. Brandewinder, J. Mattout, P. Maquet, Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis, Sleep, Volume 41, Issue suppl_1, 27 April 2018, Pages A121, https://doi.org/10.1093/sleep/zsy061.314
📃 Peter-Derex L. et al., Evaluation Of A Single-channel Automatic Sleep Analysis Software In Sleep Disorders, Sleep 2018
L. Peter-Derex, C. Berthomier, M. Brandewinder, J. Mattout, P. Berthomier, H. Bastuji, Evaluation Of A Single-channel Automatic Sleep Analysis Software In Sleep Disorders, Sleep, Volume 41, Issue suppl_1, 27 April 2018, Pages A406, https://doi.org/10.1093/sleep/zsy061.1091
📖 Dang-Vu T. T., et al., Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia, Sleep Med., 2017
Dang-Vu TT, Hatch B, Salimi A, Mograss M, Boucetta S, O’Byrne J, Brandewinder M, Berthomier C, Gouin JP., Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia, Sleep Med. 2017 Nov;39:54-61. doi: 10.1016/j.sleep.2017.08.012. BACKGROUND: While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. METHODS: Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months’ follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. RESULTS: After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. CONCLUSION: These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy. > Abstract: Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.
📖 Dang-Vu T. T., et al., Sleep spindles predict stress-related increases in sleep disturbances, Front. Hum. Neurosci., 2015
Dang-Vu T. T., Salimi A., Boucetta S., Wenzel K., O’Byrne J., M. Brandewinder M., Berthomier C. and Gouin J.-P.. Sleep spindles predict stress-related increases in sleep disturbances. Front. Hum. Neurosci., 10 February 2015 | doi: 10.3389/fnhum.2015.00068 The aim of this study was to prospectively assess whether spindle density would predict the worsening of sleep disturbances in response to a standardized stressor. We chose to follow a population of undergraduate university students during a period of increasing academic stress. In this context, assessing students at the beginning of the semester, corresponding to a lower stress period, and reevaluating them during a follow-up in the week preceding the final examinations, a period of higher stress, provides a unique opportunity to examine individual differences in the evolution of insomnia symptoms in response to a standardized stressor. > Full text: Sleep spindles predict stress-related increases in sleep disturbances
📃 O'Byrne J, et al., Spindles and slow waves predict treatment responses to cognitive-behavioural therapy for chronic primary insomnia, J. Sleep Res. 2014
O’Byrne J, Boucetta S, Reed L,Malhi O, Zhang V, Arcelin A, Wenzel K, Brandewinder M, Berthomier C, Gouin J-P, Dang-Vu TT. Spindles and slow waves predict treatment responses to cognitive-behavioural therapy for chronic primary insomnia. J. Sleep Res., 23 Suppl. 1:209, 2014 22nd ESRS Congress, Tallinn, Estonia 16 – 20 September 2014
📃 Sagaspe P, et al., Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment, J. Sleep Res. 2014
Sagaspe P, Taillard J, Chaufton C, Berthomier C, Brandewinder M, Amiéva H, Dartigues J-F, Philip P. Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment. J. Sleep Res., 23 Suppl. 1:240, 2014 22nd ESRS Congress, Tallinn, Estonia 16 – 20 September 2014
📃 Berthomier C. et al., Real-Time Automatic Measure of Drowsiness based on a Single EEG Channel, Journal of Sleep Research, 2008
Berthomier C, Muzet A, Berthomier P, Prado J, Mattout J, Real-Time Automatic Measure of Drowsiness based on a Single EEG Channel, Journal of Sleep Research, Vol 17/suppl.1,P434, 2008
19th Congress of the European Sleep Research Society (ESRS)
September 9-13, 2008, Glasgow, UK
📃 Berthomier C. et al., Real-Time Automatic Wake/Sleep Scoring based on a Single EEG Channel, Sleep 2008
Berthomier C, Herman-Stoïca M, Berthomier P, Drouot X, Prado J, Mattout J, d’Ortho MP, Real-Time Automatic Wake/Sleep Scoring based on a Single EEG Channel, Sleep, Vol 31 (suppl.), A338, 2008
22nd Annual Meeting of the Associated Professional Sleep Societies, LLC (SLEEP 2008)
June 7-12, 2008, Baltimore, MD, USA
📃 Berthomier C. et al., Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals, Sleep, 2007
Berthomier C; Drouot X; Herman-Stoïca M; Berthomier P; Prado J; Bokar-Thire D; Benoit O; Mattout J; d’Ortho MP., Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals, Sleep 2007;30(11):1587-1595.
STUDY OBJECTIVE: To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms.
DESIGN: Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis.
SETTING: Sleep laboratory in the physiology department of a teaching hospital.
PARTICIPANTS: Fifteen healthy volunteers.
MEASUREMENTS AND RESULTS: The epoch-by-epoch comparison was based on classifying into 2 states (wake/sleep), 3 states (wake/REM/ NREM), 4 states (wake/REM/stages 1-2/SWS), or 5 states (wake/REM/ stage 1/stage 2/SWS). The obtained overall agreements, as quantified by the kappa coefficient, were 0.82, 0.81, 0.75, and 0.72, respectively. Furthermore, obtained agreements between ASEEGA and the expert consensual scoring were 96.0%, 92.1%, 84.9%, and 82.9%, respectively. Finally, when classifying into 5 states, the sensitivity and positive predictive value of ASEEGA regarding wakefulness were 82.5% and 89.7%, respectively. Similarly, sensitivity and positive predictive value regarding REM state were 83.0% and 89.1%.
CONCLUSIONS: Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.
📖 Berthomier C. et al., Real-Time Automatic Measurement of Recorded Sleep Time, Chest 2007
📃 Berthomier C. et al., Wake-REM-NREM Automatic Classification based on a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Patients, J. Sleep Res., 2006
Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Benoit O, Mattout J, d’Ortho MP, Wake-REM-NREM Automatic Classification based on a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Patients, J. Sleep Res., Vol 15/suppl.1, P295, 2006 18th Congress of the European Sleep Research Society (ESRS) September 12-16, 2006, Innsbruck, Austria
📃 Berthomier C. et al., A Wake-REM-NREM Automatic Analysis Using a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Healthy Subjects, Sleep Medicine 2005
Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Mattout J, d’Ortho MP, A Wake-REM-NREM Automatic Analysis Using a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Healthy Subjects, Sleep Medicine, Vol. 6/Suppl.2 (2005), S194 First Congress of the World Association of Sleep Medicine (WASM) 15-18 October 2005, Berlin, Germany