Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems

Zhang, Yuezhou; Yang, Zhicheng; Lan, Ke; Liu, Xiaoli; Zhang, Zhengbo; Li, Peiyao; Cao, Desen; Zheng, Jiewen; Pan, Junjun

doi:10.1109/infcomw.2019.8845115

Cited by 33 publications

(11 citation statements)

References 26 publications

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“…Results show that the average accuracy of the 240 participants was 81.16%, which is a similar level of the accuracy estimated by medical experts [59]. However, N1 stage is poorly detected compared with the other stages that have been consistently addressed in the previous reports [62], and the N1 and N2 sleep stages are usually considered as one stage, 'Light sleep' [27] in four-sleep stage taxonomy [92][93][94][95][96][97]. We also applied the four stage scheme with combining the N1 and N2 stage as one 'Light sleep' to analyze the sleep quality in multiple approaches.…”

Section: Sleep Stage Automation Evaluationsupporting

confidence: 56%

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Baek

Roh

et al. 2021

Sensors

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In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

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Section: Sleep Stage Automation Evaluationsupporting

confidence: 56%

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Baek

Roh

et al. 2021

Sensors

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“…Several recent studies have used wearable devices to estimate sleep quality and sleep-related parameters [15][16][17][18] and analyzed the relationship between sleep and depression [19][20][21]. Miwa et al [19] estimated sleep quality by detecting rollover movements during sleep and observed a significant difference in sleep quality between nondepressed and depressed people.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

Zhang

Folarin

Sun

et al. 2021

JMIR Mhealth Uhealth

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Background Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

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“…The algorithms deployed on the system included signal quality evaluation, signal processing, real-time abnormal event monitoring and early prediction, and patients’ health assessment, which were packaged as a toolkit (Midas). The accuracy, stability, and effectiveness of our system have been validated in previous studies [ 44 - 46 ].…”

Section: Methodsmentioning

confidence: 86%

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Liu

Zhang

et al. 2021

JMIR Med Inform

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Background Without timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on. Objective We aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data. Methods TOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO2) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance. Results TOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO2) statistical information. Conclusions TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses.

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Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems

Cited by 33 publications

References 26 publications

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

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