Author:
Peng Shun,Li Yang,Cui Rui,Xu Ke,Wu Yonglin,Huang Ming,Dai Chenyun,Tamur Toshiyo,Mukhopadhyay Subhas,Chen Chen,Chen Wei
Abstract
Abstract
Background
Capacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition.
Methods
Two sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced.
Results
In the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment.
Conclusions
The results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.
Funder
National Natural Science Foundation of China
Shanghai Committee of Science and Technology
Shanghai Municipal Science and Technology International R&D Collaboration Project
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Cited by
1 articles.
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