scholarly journals The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

2019 ◽  
Author(s):  
Pierrick J. Arnal ◽  
Valentin Thorey ◽  
Michael E. Ballard ◽  
Albert Bou Hernandez ◽  
Antoine Guillot ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sleep Disorders ◽  
Large Scale ◽  
Absolute Error ◽  
Sleep Stages ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Sleep Staging ◽  
Breathing Frequency ◽  
Sleep Study

Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH’s automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm and 3.2 ± 0.6 %, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 ± 6.4% (F1 score : 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies.

scholarly journals 1211 Assessing the Accuracy of a Dry-EEG Headband for Measuring Brain Activity, Heart Rate, Breathing and Automatic Sleep Staging

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A463-A463
Author(s):  
V Thorey ◽  
A Guillot ◽  
K El Kanbi ◽  
M Harris ◽  
P J Arnal
Keyword(s):  
Heart Rate ◽  
Large Scale ◽  
Brain Activity ◽  
Absolute Error ◽  
Sleep Stages ◽  
Signal Acquisition ◽  
Sleep Staging ◽  
Breathing Frequency ◽  
Sleep Study ◽  
Dry Electrodes

Abstract Introduction The development of new sleep study devices, adapted for daily use, is necessary for diagnosis of sleep disorders. However, this requires to be both suitable for daily use and capable of recording accurate electrophysiological data. This study assesses the signal acquisition of a comfortable sleep headband, using dry electrodes, and the performance of its automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 4 sleep experts. Methods 42 participants slept at a sleep center wearing both the Dreem headband (DH) and a PSG simultaneously. We measured 1) the EEG signal similarity between both devices, 2) heart rate, breathing frequency and respiration rate variability (RRV) agreement, and 3) the performance of the headband automatic sleep scoring compared to PSG sleep experts manual scoring. Results Results demonstrate a strong correlation between the EEG signals acquired by the headband and those from the PSG, and the signals acquired by the headband enable monitoring of alpha (r= 0.75 ± 0.11), beta (r= 0.74 ± 0.14), delta (r = 0.78 ± 0.16), and theta (r = 0.63 ± 0.15) frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 2.2 ± 0.8 bpm, 0.3 ± 0.2 cpm and 3.1 ± 0.4 %, respectively. Automatic Sleep Staging reached an overall accuracy of 84.1 ± 7.5% (F1 score: 83.0 ± 8.4) for the headband to be compared with an average of 86.4 ± 5.5% (F1 score: 86.5 ± 5.5) for the 4 sleep experts. Conclusion These results demonstrate the capacity of the headband to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Support This Study has been supported by Dreem sas.

scholarly journals The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging

SLEEP ◽  
2020 ◽  
Vol 43 (11) ◽  
Author(s):  
Pierrick J Arnal ◽  
Valentin Thorey ◽  
Eden Debellemaniere ◽  
Michael E Ballard ◽  
Albert Bou Hernandez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Large Scale ◽  
Percentage Error ◽  
Sleep Stages ◽  
Signal Acquisition ◽  
Sleep Staging ◽  
Breathing Frequency ◽  
Clinical Trial Registration ◽  
Sleep Study ◽  
The Mean

Abstract Study Objectives The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-electroencephalographic (EEG) device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by five sleep experts. Methods A total of 25 subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. We assessed (1) similarity of measured EEG brain waves between the DH and the PSG; (2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG; and (3) the performance of the DH’s automatic sleep staging according to American Academy of Sleep Medicine guidelines versus PSG sleep experts manual scoring. Results The mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of α was 15 ± 3.5%, 16 ± 4.3% for β, 16 ± 6.1% for λ, and 10 ± 1.4% for θ frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm, and 3.2 ± 0.6%, respectively. Automatic sleep staging reached an overall accuracy of 83.5 ± 6.4% (F1 score: 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the 5 sleep experts. Conclusions These results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for, large-scale, longitudinal sleep studies. Clinical Trial Registration NCT03725943.

scholarly journals 1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A460-A461
Author(s):  
E P Pollet ◽  
D P Pollet ◽  
B Long ◽  
A A Qutub
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Sleep Study ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Large Populations

Abstract Introduction Fitness-based wearables and other emerging sensor technologies have the potential to track sleep across large populations longitudinally in at-home environments. To understand how these devices can inform research studies, limitations of available trackers need to be compared to traditional polysomnography (PSG). Here we assessed discrepancies in sleep staging in activity trackers vs. PSG in subjects with various sleep disorders. Methods Twelve subjects (age 41-78, 7f, 5m) wore a Fitbit Charge 3 while undergoing a scheduled sleep study. Six subjects had been previously diagnosed with a sleep disorder (5 OSA, 1 CSA). 4 subjects used CPAP throughout the night, 2 had a split night (CPAP 2nd half of the night), and 6 had a PSG only. Activity tracker staging was compared to 2 RPSGTs staging. Results Of the 12 subjects, eight subjects’ sleep was detected in the activity tracker, and compared across sleep stages to the PSG (7 female, 1 male, ages 41-78, AHI 0.3-87, RDI 0.5-94.4, sleep efficiency 74%+/-18, 4 PSG, 1 split, 3 CPAP). The activity tracker matched either tech 52% (+/- 13). The average difference in score tech and activity tracker staging for sleep onset (SO) was 16 +/- 15 minutes and wake after sleep onset was 43.5 +/- 44 minutes. Sensitivity, specificity, and balanced accuracy were found for each sleep stage. Respectively, Wake: 0.45+/-0.27, 0.97+/-0.03, 0.71+/-0.12, REM: 0.41+/-0.30, 0.90+/-0.06, 0.60+/-0.28, Light: 0.71+/-0.09, 0.58+/-0.19, 0.65+/-0.10, Deep: 0.63+/-0.52, 0.88+/-0.05, 0.59+/-0.49. Conclusion From this study of 12 subjects seen at a sleep clinic for suspected sleep disorders, activity trackers performed best in wake, REM and deep sleep specificity (>=88%), while they lacked sensitivity to REM and wake (<=45%) stages. The tracker did not detect sleep in 4 subjects who had elevated AHI or low sleep efficiency. Further analysis can identify whether discrepancies between the Fitbit and PSG can be predicted by distinct patterns in sleep staging and/or identify subject exclusion criteria for activity tracking studies. Support This project in on-going with the support of Academy Diagnostics Sleep and EEG Center and staff.

scholarly journals Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Pedro Fonseca ◽  
Merel M van Gilst ◽  
Mustafa Radha ◽  
Marco Ross ◽  
Arnaud Moreau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Disorders ◽  
Recurrent Neural Networks ◽  
Clinical Diagnostics ◽  
Sleep Staging ◽  
Substantial Agreement ◽  
Body Movements ◽  
Wide Range

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

scholarly journals 0447 ResTNet: A Robust End-to-End Deep Learning Approach to Sleep Staging of Self Applied Somnography Studies

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A171-A171
Author(s):  
S Æ Jónsson ◽  
E Gunnlaugsson ◽  
E Finssonn ◽  
D L Loftsdóttir ◽  
G H Ólafsdóttir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Quality Data ◽  
Sleep Stages ◽  
Learning Approach ◽  
Sleep Staging ◽  
Self Administration ◽  
Sleep Study ◽  
Sleep Studies ◽  
End To End

Abstract Introduction Sleep stage classifications are of central importance when diagnosing various sleep-related diseases. Performing a full PSG recording can be time-consuming and expensive, and often requires an overnight stay at a sleep clinic. Furthermore, the manual sleep staging process is tedious and subject to scorer variability. Here we present an end-to-end deep learning approach to robustly classify sleep stages from Self Applied Somnography (SAS) studies with frontal EEG and EOG signals. This setup allows patients to self-administer EEG and EOG leads in a home sleep study, which reduces cost and is more convenient for the patients. However, self-administration of the leads increases the risk of loose electrodes, which the algorithm must be robust to. The model structure was inspired by ResNet (He, Zhang, Ren, Sun, 2015), which has been highly successful in image recognition tasks. The ResTNet is comprised of the characteristic Residual blocks with an added Temporal component. Methods The ResTNet classifies sleep stages from the raw signals using convolutional neural network (CNN) layers, which avoids manual feature extraction, residual blocks, and a gated recurrent unit (GRU). This significantly reduces sleep stage prediction time and allows the model to learn more complex relations as the size of the training data increases. The model was developed and validated on over 400 manually scored sleep studies using the novel SAS setup. In developing the model, we used data augmentation techniques to simulate loose electrodes and distorted signals to increase model robustness with regards to missing signals and low quality data. Results The study shows that applying the robust ResTNet model to SAS studies gives accuracy > 0.80 and F1-score > 0.80. It outperforms our previous model which used hand-crafted features and achieves similar performance to a human scorer. Conclusion The ResTNet is fast, gives accurate predictions, and is robust to loose electrodes. The end-to-end model furthermore promises better performance with more data. Combined with the simplicity of the SAS setup, it is an attractive option for large-scale sleep studies. Support This work was supported by the Icelandic Centre for Research RANNÍS (175256-0611).

SCORING SLEEP: THE RULES FOR LOOKING INSIDE

2015 ◽  
Vol 14 (3) ◽  
pp. 119-124
Author(s):  
Floriana Boghez ◽  
◽  
Ioana Mandruta ◽  
Keyword(s):  
Eye Movements ◽  
Sleep Disorders ◽  
Diagnostic Tool ◽  
Video Recording ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Sleep Study ◽  
Rapid Eye Movements ◽  
Cardiac Functions ◽  
Important Diagnostic Tool

Polysomnography is the most comprehensive sleep study, a multi-parametric recording test (electroencephalography, electrooculogram, chin and limbs electromyogram, respiratory and cardiac functions and permanent video-recording), used as an important diagnostic tool for the sleep disorders. Sleep architecture or the sleep macrostructure is a term used to describe the divisions of sleep into specific sleep stages using electro encephalographic (EEG), electrooculographic (EOG) and electromyographic (EMG) criteria: NREM (non-rapid eye movements) stages – N1, N2 and N3, and REM (rapid eye movements) stage.

247 Automated sleep staging using wrist-worn device and deep neural networks

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A100-A100
Author(s):  
Niranjan Sridhar ◽  
Atiyeh Ghoreyshi ◽  
Lance Myers ◽  
Zachary Owens
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Population Level ◽  
Health Study ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Free Living ◽  
Cohen’S Kappa ◽  
Class Prediction ◽  
Cohen's Kappa

Abstract Introduction Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. Methods We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM. The largest probability is chosen as the network’s class prediction and used to form the hypnogram. We used the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis Study (MESA) and Physionet Computing in Cardiology (CinC) dataset (over 10000 nights) for training and evaluation. Then we deployed the algorithm on PPG based heart rate measured by a wrist-worn device worn by subjects in a free-living setting. Results On the held out test SHHS dataset (800 nights, 561 subjects), the overall 4-class staging accuracy was 77% and Cohen’s kappa was 0.66. On the CinC dataset (993 nights, 993 subjects), the overall 4 class accuracy was 72% and Cohen’s kappa was 0.55. The study on free-living subjects is underway and these novel results will be collated and presented upon completion. Conclusion We hope these results build more trust in automated heart rate based sleep staging and encourage further research into its clinical application in screening and diagnosis of sleep disorders. Low cost, high efficacy devices which can be used in longitudinal studies can lead to breakthroughs in clinical applications of sleep staging for early diagnosis of chronic conditions and novel treatment endpoints. Support (if any) We recently published the training/testing of the algorithm as well a population level analysis showing differences in predicted sleep stages between disease cohorts. The article was published in NPJ Digital Medicine in Aug 2020. The study on free living subjects is currently underway and these new results will be presented at the sleep conference. Preliminary results indicate high concordance with our published results.

399 Using Machine Learning to Inform Extraction of Clinical Data from Sleep Study Reports

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A158-A159
Author(s):  
Diego Mazzotti ◽  
Bethany Staley ◽  
Brendan Keenan ◽  
Allan Pack ◽  
Richard Schwab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sleep Disorders ◽  
Large Scale ◽  
Data Extraction ◽  
Supervised Machine Learning ◽  
Receiver Operating Curve ◽  
Sleep Study ◽  
Study Type ◽  
Study Format

Abstract Introduction In-laboratory and home sleep studies are important tools for diagnosing sleep disorders. However, a limited amount of measurements is used to inform disease severity and only specific measures, if any, are stored as structured fields into electronic health records (EHR). We propose a sleep study data extraction approach based on supervised machine learning to facilitate the development of specialized format-specific parsers for large-scale automated sleep data extraction. Methods Using retrospective data from the Penn Medicine Sleep Center, we identified 64,100 sleep study reports stored in Microsoft Word documents of varying formats, recorded from 2001–2018. A random sample of 200 reports was selected for manual annotation of formats (e.g., layout) and type (e.g. baseline, split-night, home sleep apnea tests). Using text mining tools, we extracted 71 document property features (e.g., section dimensions, paragraph and table elements, regular expression matches). We identified 14 different formats and 7 study types. We used these manual annotations as multiclass outcomes in a random forest classifier to evaluate prediction of sleep study format and type using document property features. Out-of-bag (OOB) error rates and multiclass area under the receiver operating curve (mAUC) were estimated to evaluate training and testing performance of each model. Results We successfully predicted sleep study format and type using random forest classifiers. Training OOB error rate was 5.6% for study format and 8.1% for study type. When evaluating these models in independent testing data, the mAUC for classification of study format was 0.85 and for study type was 1.00. When applied to the large universe of diagnostic sleep study reports, we successfully extracted hundreds of discrete fields in 38,252 reports representing 33,696 unique patients. Conclusion We accurately classified a sample of sleep study reports according to their format and type, using a random forest multiclass classification method. This informed the development and successful deployment of custom data extraction tools for sleep study reports. The ability to leverage these data can improve understanding of sleep disorders in the clinical setting and facilitate implementation of large-scale research studies within the EHR. Support (if any) American Heart Association (20CDA35310360).

751 Heart Rate and Heart Rate Variability During Sleep As Biomarkers for Depression

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A293-A293
Author(s):  
Massimiliano Grassi ◽  
Daniela Caldirola ◽  
Silvia Daccò ◽  
Giampaolo Perna ◽  
Archie Defillo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Sleep Wake Disorders ◽  
Sleep Study ◽  
Increase Risk ◽  
Patient Health ◽  
Nervous System Function ◽  
Specific Alteration

Abstract Introduction Evidence suggests a high prevalence of depression in subjects with Sleep-Wake Disorders, with impaired sleep being both a risk factor and a symptom of depression. However, depression currently remains for the most undiagnosed in this population, which can lead to a lack or delay in the treatment, and ultimately contribute to chronicity, recurrence of depression, and increase risk of suicide. Depression is characterized by alteration in sleep architecture and imbalanced autonomic nervous system function, and specific alteration may serve as biomarkers to identify ongoing depression in subjects with Sleep-Wake Disorders undergoing polysomnography. Thus, the aim of this study is to investigate differences in sleep architecture and autonomic modulation, measured by heart rate and heart rate variability throughout sleep stages, in subjects undergoing polysomnography in a sleep clinic. Methods A preliminary sample of forty subjects undergoing polysomnography was recruited in three different sleep clinics. The Patient Health Questionnaire–9 was administered to participants before the beginning of the sleep study. A cut-off of 10 was applied to identify subjects with possible current depression. The polysomnography recordings were processed with the MEBsleep software (Medibio Limited) which automatically calculats sleep architecture indices, and heart rate and heart rate variability parameters throughout sleep stages. The Mann-Whitney U test was used to investigate differences between the depressed and non-depressed groups. Results Possible current depression was found in fourteen subjects (35%). These Subjects had statistically significant higher heart rate (median depressed=78.01, median non-depressed=64.61, p=0.01) and lower Root Mean Square of the Successive Difference (RMSSD; median depressed=18.41 ms, median non-depressed=26.52 ms, p=0.02), number of pairs of successive NN intervals that differ by more than 50 ms (pNN50. Median depressed=1.62%; median non-depressed=5.64%; p=0.03), and High Frequency (absolute power) in REM (median depressed=104.17 ms2; median non-depressed=214.58 ms2; p=0.03) than those without depression. No significant differences resulted in the sleep architecture indices. Conclusion These results preliminary indicates a decreased parasympathetic activity in subjects with possible depression during REM, suggesting that heart rate and heart rate variability during sleep may be used as biomarkers to identify current depression in subjects undergoing polysomnography in sleep clinics. Support (if any):

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