Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Lazazzera, Remo; Laguna, Pablo; Gil, Eduardo; Carrault, Guy

doi:10.3390/s21237976

Cited by 8 publications

(5 citation statements)

References 72 publications

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“…Given the growing use of wearable systems to monitor physiological signals during sleep, our Special Issue could not miss the contributions regarding this crucial aspect that significantly influences an individual’s quality of life. Lagazzera et al presented UpNEA, a novel sleep-monitoring platform based on a smart glove, recording PPG, SpO2, and three accelerometer signals, a mobile application and a remote server [ 15 ]. The machine learning algorithms used for apnea and hypopnea detection showed promising results in highlighting sleep-disruptive breathing events and classifying them.…”

Section: Contributionsmentioning

confidence: 99%

Advances in Wearable Photoplethysmography Applications in Health Monitoring

Nardelli

Bailón

2023

Sensors

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Section: Contributionsmentioning

confidence: 99%

Advances in Wearable Photoplethysmography Applications in Health Monitoring

Nardelli

Bailón

2023

Sensors

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“…Specifically, HR is a direct metric of how healthy the cardiovascular system is, hence, measuring and estimating HR is crucial in many aspects, including public health oversight, sleep monitoring, and control of exercise intensity. Because HR fluctuates substantially with psychological aspects, it is widely used in lie detection, sleep monitoring, fatigue driving, and so on [ 1 , 2 , 3 ]. In general, we estimate HR by electrocardiography (ECG) in the clinic, which is measured by sensors that capture weak electrical activity generated by the cardiac cycle when the heart beats.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Estimation from Incomplete Electrocardiography Signals

Song

Chen

Zhang

2023

Sensors

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As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

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“…Meanwhile, moderate-to-severe OSA is a common cause of insomnia in patients (Hein et al, 2017). Lazazzera et al (2021) have designed a sleep monitoring platform that detects apnea and hypoventilation with a correct rate of up to 75.1%. At night patients need to wear smart gloves for signal acquisition and open a smartphone application where the information is transmitted to a remote server for cloud computing to estimate the status of sleep, breathing and heart rate during the night.…”

Section: Introductionmentioning

confidence: 99%

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

et al. 2022

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BackgroundOSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.MethodWe collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.ResultsThere are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.ConclusionThe prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

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Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Cited by 8 publications

References 72 publications

Advances in Wearable Photoplethysmography Applications in Health Monitoring

Advances in Wearable Photoplethysmography Applications in Health Monitoring

Heart Rate Estimation from Incomplete Electrocardiography Signals

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

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