Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

Li, Qiao; Li, Qichen; Liu, Chengyu; Shashikumar, Supreeth P.; Nemati, Shamim; Clifford, Gari D.

doi:10.1088/1361-6579/aaf339

Cited by 75 publications

(64 citation statements)

References 45 publications

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“…The current state-of-the-art results extract dozens, if not hundreds, of expert-informed engineered features from physiological signals such as ECG/PPG and then use these as inputs to complex machine learning models to predict sleep stages [19][20][21][22] . The models either use features of neighboring time epochs explicitly to predict the stage for each time epoch 19 or use implicitly temporal models such as recurrent neural networks (RNNs) 20 . Such models have two important drawbacks that we address with our novel architecture: strength of the correlation is between cardiac features and PSG sleep stages it is not known a priori.…”

Section: Discussionmentioning

confidence: 99%

“…The per-class performance (recall, precision) of the algorithm was wake-(0.80, 0.86), light-(0.82, 0.74), deep-(0.49, 0.68), REM-(0.81, 0.76) on SHHS and wake-(0.74, 0.61), light-(0.76, 0.79), deep-(0.48, 0.67), REM-(0.76, 0.66) on CinC. For comparison, the previously published best 4 class accuracy (kappa) on these datasets was 66% (0.47) on SHHS and 65% (0.31) on CinC using ECG-derived signals 19 .…”

Section: Model Performancementioning

confidence: 92%

“…The best published per-class performance (recall, precision) on these datasets was wake-(0.75, 0.73), light-(0.62, 0.84), deep-(0.59, 0.13), REM-(0.61, 0.39) on SHHS 19 . Figure 4a, e demonstrate that the model's per night accuracy is high both on the dataset it was trained on and a new independent dataset.…”

Section: Model Performancementioning

confidence: 99%

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Deep learning for automated sleep staging using instantaneous heart rate

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Model Performancementioning

confidence: 92%

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Deep learning for automated sleep staging using instantaneous heart rate

et al. 2020

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“…Some studies have been based on the deep learning framework for automatic sleep stage scoring using the ECG signal [19][20][21]. In these studies, deep learning frameworks (DNN [19], CNN [20], RNN [20], and LSTM [21]) were used as feature extractors or classifiers in previous studies based on the ECG signal. In addition, they demonstrated less accuracy in the three-class sleep stage classification than the current study.…”

Section: Discussionmentioning

confidence: 99%

Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit

Urtnasan¹,

Kim²,

Park³

et al. 2020

IJFIS

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A healthy sleep structure is clinically very important for overall health. The sleep structure can be represented by the percentage of different sleep stages during the total sleep time. In this study, we proposed a method for automatic classification of sleep stages from an electrocardiogram (ECG) signal using a gated-recurrent unit (GRU). The proposed method performed multiclass classification for three-class sleep stages such as awake, light, and deep sleep. A deep structured GRU was used in the proposed method, which is a common recurrent neural network. The proposed deep learning (SleepGRU) model consists of a 5-layer GRU and is optimized by batch-normalization, dropout, and Adam update rules. The ECG signal was recorded during nocturnal polysomnography from 112 subjects, and was normalized and segmented into units of 30-second duration. To train and evaluate the proposed method, the training set consisted of 80,316 segments from 89 subjects, and the test set used 20,079 segments from 23 subjects. We achieved good performances with an overall accuracy of 80.43% and F1-score of 80.07% for the test set. The proposed method can be an alternative and useful tool for sleep monitoring and sleep screening, which have previously been manually evaluated by a sleep technician or sleep expert.

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“…As mentioned above, there is a strong relation between the length and transition of different sleep stages, especially SWS and the quality of sleep [33][34][35][36][37]. This relation has been widely studied using PSG, for instance, [49][50][51][52] demonstrate distinguishability of sleep stages based on the physiological signals. However, using PSG is cost prohibited and intrusively effects sleep quality of participants.…”

Section: Literature Reviewmentioning

confidence: 97%

Sleep quality prediction in caregivers using physiological signals

Sadeghi

Banerjee

Hughes

et al. 2019

Computers in Biology and Medicine

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Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

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Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

Cited by 75 publications

References 45 publications

Deep learning for automated sleep staging using instantaneous heart rate

Deep learning for automated sleep staging using instantaneous heart rate

Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit

Sleep quality prediction in caregivers using physiological signals

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