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 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 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 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).

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.

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.

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):

scholarly journals Deep learning for automated sleep staging using instantaneous heart rate

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Niranjan Sridhar ◽  
Ali Shoeb ◽  
Philip Stephens ◽  
Alaa Kharbouch ◽  
David Ben Shimol ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Massachusetts General Hospital ◽  
Sleep Stage ◽  
Health Study ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Instantaneous Heart Rate ◽  
And Gender ◽  
Electrocardiogram Ecg

Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

Benchmark of Simplified Time-Dependent Density Functional Theory for UV-Vis Spectral Properties of Porphyrinoids

2019 ◽  
Author(s):  
Kamal Batra ◽  
Stefan Zahn ◽  
Thomas Heine
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Large Scale ◽  
Absolute Error ◽  
Time Dependent ◽  
Basis Set ◽  
Basis Sets ◽  
Functional Theory ◽  
Framework Materials ◽  
Dependent Density

<p>We thoroughly benchmark time-dependent density- functional theory for the predictive calculation of UV/Vis spectra of porphyrin derivatives. With the aim to provide an approach that is computationally feasible for large-scale applications such as biological systems or molecular framework materials, albeit performing with high accuracy for the Q-bands, we compare the results given by various computational protocols, including basis sets, density-functionals (including gradient corrected local functionals, hybrids, double hybrids and range-separated functionals), and various variants of time-dependent density-functional theory, including the simplified Tamm-Dancoff approximation. An excellent choice for these calculations is the range-separated functional CAM-B3LYP in combination with the simplified Tamm-Dancoff approximation and a basis set of double-ζ quality def2-SVP (mean absolute error [MAE] of ~0.05 eV). This is not surpassed by more expensive approaches, not even by double hybrid functionals, and solely systematic excitation energy scaling slightly improves the results (MAE ~0.04 eV). </p>

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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