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. 2022 Oct 13;21(1):75.
doi: 10.1186/s12938-022-01031-5.

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks

Shun Peng #  1 , Yang Li #  1 , Rui Cui  1 , Ke Xu  2 , Yonglin Wu  1 , Ming Huang  3 , Chenyun Dai  1 , Toshiyo Tamur  4 , Subhas Mukhopadhyay  5 , Chen Chen  6 , Wei Chen  7   8
Affiliations

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks

Shun Peng et al. Biomed Eng Online. .

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.

Keywords: Capacitive electrocardiogram; Capacitively coupled electrode; Recurrent neural network; Sleep posture.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Confusion matrices of sleep posture prediction in short-term data where the length of each data segment is one heat beat (a) or 30 s (b)
Fig. 2
Fig. 2
Confusion matrices of sleep posture prediction in overnight data where the length of each data segment is one heat beat (a) or 30 s (b)
Fig. 3
Fig. 3
ECG vector projection onto the three limb leads (leads I, II and III) (a) and the cECG of three sleep postures (b) [36]. The ring-shaped dotted line represents the ECG vector during ventricular depolarization. Three sleep postures include supine, left lateral and right lateral
Fig. 4
Fig. 4
Smart mattress: a the system frame; b ECG acquisition channel; c the hardware of data acquisition and transmission; d the mattress structure; e the prototype of the smart mattress
Fig. 5
Fig. 5
The experimental scene images: a raw signal of channel 1; b filtered signal of channel 1; c raw signal of channel 2; d filtered signal of channel 2
Fig. 6
Fig. 6
Simultaneously recorded body position signal (L: left lateral. S: supine. R: right lateral) and cECG signal in the overnight experiment
Fig. 7
Fig. 7
Network structure of the model for sleep posture classification
See this image and copyright information in PMC

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