Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data

Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.; Castriotta, Richard J.

doi:10.3390/s21010025

Cited by 12 publications

(6 citation statements)

References 34 publications

(48 reference statements)

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“…The accuracy of RM-OSM ranges from 76.2% to With the exception of single methods, mix methods are adopted, including ACT & ECG, 240,241 ACT & RM, 236,242 ECG & RM, 227,[243][244][245][246] ECG & OSM, 247 RM & OSM, 236 ACT & ECG & RM, 248 and ACT & RM & OSM, 236 their accuracies range from 69% to 95.7%, with an average accuracy of 84.16%. As compared to single methods, mix methods were more accurate and better at sleep staging.…”

Section: Accuracymentioning

confidence: 99%

Objective sleep assessments for healthy people in environmental research: A literature review

Lian

2022

Indoor Air

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To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring‐oxygen saturation monitoring (RM‐OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM‐OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non‐contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

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Section: Accuracymentioning

confidence: 99%

Objective sleep assessments for healthy people in environmental research: A literature review

Lian

2022

Indoor Air

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“…Modern consumer wearables remain limited in their ability to assess these parameters and should not yet be considered substitutes for gold standard polysomnography. 90,91…”

Section: Assessment Of Sleep Quality and Timingmentioning

confidence: 99%

“…Though the application of artificial intelligence deep learning algorithms incorporating wearable‐generated data appears promising, this is a growing area of research for which the evidence base remains limited. Modern consumer wearables remain limited in their ability to assess these parameters and should not yet be considered substitutes for gold standard polysomnography 90,91 …”

Section: Assessment Of Sleep Quality and Timingmentioning

confidence: 99%

“…Modern consumer wearables remain limited in their ability to assess these parameters and should not yet be considered substitutes for gold standard polysomnography. 90,91 Assessment of Ambulatory Blood Pressure Primary hypertension is seen in nearly half of adults in the United States and contributes to an elevated risk of stroke, myocardial infarction, and mortality, especially in the perioperative setting. [92][93][94][95] Select cuff-based wristwatches, such as the Omron HeartGuide, provide an intermittent reading of ambulatory blood pressure.…”

Section: Assessment Of Sleep Quality and Timingmentioning

confidence: 99%

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Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Somani

Chiu

et al. 2021

Otolaryngol.--head neck surg.

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Objective Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients. Data Sources PubMed. Review Methods A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review. Conclusions In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients’ engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care. Implications for Practice Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

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“…One example is actigraphy, which has been widely applied in sleep research owing to its advantages of cost-efficiency and reduced influence on sleep [ 6 ]. Actigraphy-based sleep scoring has been further encouraged with the recent use of machine or deep learning algorithms [ 7 , 8 ] or additional physiological parameters such as heart rate variability [ 9 , 10 ]. However, the actigraphy-based sleep scoring algorithms suffer from low specificity, that is, low performance in detecting Wake epochs (between 0.28 and 0.67 [ 11 ]), and distinguishing different sleep stages with actigraphy is even more challenging.…”

Section: Introductionmentioning

confidence: 99%

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Haghayegh

Stone

et al. 2023

J Med Internet Res

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Background Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. Objective We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). Methods SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). Results The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. Conclusions SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.

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Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data

Cited by 12 publications

References 34 publications

Objective sleep assessments for healthy people in environmental research: A literature review

Objective sleep assessments for healthy people in environmental research: A literature review

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

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