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.
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.
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
Maire M., et al. Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement, Sci Rep. 2018
Maire M, Reichert CF, Gabel V, Viola AU, Phillips C, Berthomier C, Borgwardt S, Cajochen C, Schmidt C. Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement. Sci Rep. 2018 Jan 17;8(1):970. doi: 10.1038/s41598-017-17022-9.
Sleepiness and cognitive function vary over the 24-h day due to circadian and sleep-wake-dependent mechanisms. However, the underlying cerebral hallmarks associated with these variations remain to be fully established. Using functional magnetic resonance imaging (fMRI), we investigated brain responses associated with circadian and homeostatic sleep-wake-driven dynamics of subjective sleepiness throughout day and night. Healthy volunteers regularly performed a psychomotor vigilance task (PVT) in the MR-scanner during a 40-h sleep deprivation (high sleep pressure) and a 40-h multiple nap protocol (low sleep pressure). When sleep deprived, arousal-promoting thalamic activation during optimal PVT performance paralleled the time course of subjective sleepiness with peaks at night and troughs on the subsequent day. Conversely, task-related cortical activation decreased when sleepiness increased as a consequence of higher sleep debt. Under low sleep pressure, we did not observe any significant temporal association between PVT-related brain activation and subjective sleepiness. Thus, a circadian modulation in brain correlates of vigilant attention was only detectable under high sleep pressure conditions. Our data indicate that circadian and sleep homeostatic processes impact on vigilant attention via specific mechanisms; mirrored in a decline of cortical resources under high sleep pressure, opposed by a subcortical « rescuing » at adverse circadian times.
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Dang-Vu T. T., et al. Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia, Sleep Med., 2017
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.
O'Byrne J, et al., Spindles and slow waves predict treatment responses to cognitive-behavioural therapy for chronic primary insomnia, J. Sleep Res. 2014, Tallinn, Estonia.
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
Sagaspe P, et al., Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment, J. Sleep Res. 2014, Tallinn, Estonia.
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
Eichenlaub JB, et al., Brain Reactivity Differentiates Subjects with High and Low Dream Recall Frequencies during Both Sleep and Wakefulness, Cereb. Cortex 2014
Eichenlaub JB, Bertrand O, Morlet D, Ruby P. Brain Reactivity Differentiates Subjects with High and Low Dream Recall Frequencies during Both Sleep and Wakefulness. Cereb. Cortex 24 (5): 1206-1215. 2014
The neurophysiological correlates of dreaming remain unclear. According to the « arousal-retrieval » model, dream encoding depends on intrasleep wakefulness. Consistent with this model, subjects with high and low dream recall frequency (DRF) report differences in intrasleep awakenings. This suggests a possible neurophysiological trait difference between the 2 groups. To test this hypothesis, we compared the brain reactivity (evoked potentials) of subjects with high (HR, N = 18) and low (LR, N = 18) DRF during wakefulness and sleep. During data acquisition, the subjects were presented with sounds to be ignored (first names randomly presented among pure tones) while they were watching a silent movie or sleeping. Brain responses to first names dramatically differed between the 2 groups during both sleep and wakefulness. During wakefulness, the attention-orienting brain response (P3a) and a late parietal response were larger in HR than in LR. During sleep, we also observed between-group differences at the latency of the P3a during N2 and at later latencies during all sleep stages. Our results demonstrate differences in the brain reactivity of HR and LR during both sleep and wakefulness. These results suggest that the ability to recall dreaming is associated with a particular cerebral functional organization, regardless of the state of vigilance.
Ruby P, et al., Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness, PLoS ONE 2013
Ruby P, Blochet C, Eichenlaub J-B, Bertrand O, Morlet D, Bidet-Caulet A. Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness. PLoS ONE 8(11): e79989, 2013.
We aimed at better understanding the brain mechanisms involved in the processing of alerting meaningful sounds during sleep, investigating alpha activity. During EEG acquisition, subjects were presented with a passive auditory oddball paradigm including rare complex sounds called Novels (the own first name – OWN, and an unfamiliar first name – OTHER) while they were watching a silent movie in the evening or sleeping at night. During the experimental night, the subjects’ quality of sleep was generally preserved. During wakefulness, the decrease in alpha power (8–12 Hz) induced by Novels was significantly larger for OWN than for OTHER at parietal electrodes, between 600 and 900 ms after stimulus onset. Conversely, during REM sleep, Novels induced an increase in alpha power (from 0 to 1200 ms at all electrodes), significantly larger for OWN than for OTHER at several parietal electrodes between 700 and 1200 ms after stimulus onset. These results show that complex sounds have a different effect on the alpha power during wakefulness (decrease) and during REM sleep (increase) and that OWN induce a specific effect in these two states. The increased alpha power induced by Novels during REM sleep may 1) correspond to a short and transient increase in arousal; in this case, our study provides an objective measure of the greater arousing power of OWN over OTHER, 2) indicate a cortical inhibition associated with sleep protection. These results suggest that alpha modulation could participate in the selection of stimuli to be further processed during sleep.
Ruby P, et al., Alpha reactivity to first names differs in subjects with high and low dream recall frequency, Front Psychol 2013
Ruby P, Blochet C, Eichenlaub JB, Bertrand O, Morlet D, Bidet-Caulet A. Alpha reactivity to first names differs in subjects with high and low dream recall frequency. Front Psychol.;4:419, 2013.
Studies in cognitive psychology showed that personality (openness to experience, thin boundaries, absorption), creativity, nocturnal awakenings, and attitude toward dreams are significantly related to dream recall frequency (DRF). These results suggest the possibility of neurophysiological trait differences between subjects with high and low DRF. To test this hypothesis we compared sleep characteristics and alpha reactivity to sounds in subjects with high and low DRF using polysomnographic recordings and electroencephalography (EEG). We acquired EEG from 21 channels in 36 healthy subjects while they were presented with a passive auditory oddball paradigm (frequent standard tones, rare deviant tones and very rare first names) during wakefulness and sleep (intensity, 50 dB above the subject’s hearing level). Subjects were selected as High-recallers (HR, DRF = 4.42 ± 0.25 SEM, dream recalls per week) and Low-recallers (LR, DRF = 0.25 ± 0.02) using a questionnaire and an interview on sleep and dream habits. Despite the disturbing setup, the subjects’ quality of sleep was generally preserved. First names induced a more sustained decrease in alpha activity in HR than in LR at Pz (1000–1200 ms) during wakefulness, but no group difference was found in REM sleep. The current dominant hypothesis proposes that alpha rhythms would be involved in the active inhibition of the brain regions not involved in the ongoing brain operation. According to this hypothesis, a more sustained alpha decrease in HR would reflect a longer release of inhibition, suggesting a deeper processing of complex sounds than in LR during wakefulness. A possibility to explain the absence of group difference during sleep is that increase in alpha power in HR may have resulted in awakenings. Our results support this hypothesis since HR experienced more intra sleep wakefulness than LR (30 ± 4 vs. 14 ± 4 min). As a whole our results support the hypothesis of neurophysiological trait differences in high and low-recallers.
Schmidt C, et al., Circadian and homeostatic modulation of cognition-related cerebral activity in humans, J. Sleep Res. 2013
Schmidt C, Maire M, Reichert C F, Scheffler K, Klarhoefer M, Strobel W, Krebs J, Berthomier P, Berthomier C, Cajochen C. Circadian and homeostatic modulation of cognition-related cerebral activity in humans. J. Sleep Res. 21 (Suppl. 1), 1-371, 2012.
Schmidt C, et al., Circadian preference modulates the neural substrate of conflict processing across the day, PLoS One 2012
Schmidt C, Peigneux P, Leclercq Y, Sterpenich V, Vandewalle G, Philips C, Berthomier P, Berthomier C, Tinguely G, Gais S, Schabus M, Desseilles M, Dang-Vu T, Salmon E, Degueldre C, Balteau E, Luxen A, Cajochen C, Maquet P, Collette F. Circadian preference modulates the neural substrate of conflict processing across the day. PLoS One. 2012;7(1):e29658. 2012 Jan 4. PLoS One. 2012;7(1):e29658. 2012 Jan 4.
Human morning and evening chronotypes differ in their preferred timing for sleep and wakefulness, as well as in optimal daytime periods to cope with cognitive challenges. Recent evidence suggests that these preferences are not a simple by-product of socio-professional timing constraints, but can be driven by inter-individual differences in the expression of circadian and homeostatic sleep-wake promoting signals. Chronotypes thus constitute a unique tool to access the interplay between those processes under normally entrained day-night conditions, and to investigate how they impinge onto higher cognitive control processes. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on conflict processing-related cerebral activity throughout a normal waking day. Sixteen morning and 15 evening types were recorded at two individually adapted time points (1.5 versus 10.5 hours spent awake) while performing the Stroop paradigm. Results show that interference-related hemodynamic responses are maintained or even increased in evening types from the subjective morning to the subjective evening in a set of brain areas playing a pivotal role in successful inhibitory functioning, whereas they decreased in morning types under the same conditions. Furthermore, during the evening hours, activity in a posterior hypothalamic region putatively involved in sleep-wake regulation correlated in a chronotype-specific manner with slow wave activity at the beginning of the night, an index of accumulated homeostatic sleep pressure. These results shed light into the cerebral mechanisms underlying inter-individual differences of higher-order cognitive state maintenance under normally entrained day-night conditions.