Key result
A novel CNN-based framework for capacitive ECG achieved 0.99 precision and 0.99 recall for both signal quality classification and sleep position recognition.
Why the study?
Exploration of capacitive ECG is hindered by frequent signal quality fluctuations caused by body movement and changes in sleep position, requiring adapted signal-processing frameworks.
A novel CNN-based framework demonstrates high precision and recall for classifying signal quality and sleep position from capacitive ECG measurements, facilitating unconstrained heart monitoring.
Answers are grounded in this paper and cite their sources.
May advance contactless ECG research during sleep; leaves open prospective clinical validation.
The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new signal-processing framework that determines the signal quality for short signal segments (2 and 4 seconds) using a multi-class classification model (qua_model) based on a convolutional neural network (CNN). We built another independent deep CNN classifier (pos_model) to classify the sleep position. In the validation, 12 subjects were recruited for a 30-minute experiment, which required the subjects to lie on a bed in different sleeping positions. The short segments, classified as clear (C1 class) by the qua_model, were used to determine sleep positions with the pos_model. In 10-fold cross-validation, the qua_model for signals of 4-second length could recognize the signal of the C1 class at a 0.99 precision and a 0.99 recall; the pos_model could recognize the supine sleep position, the left, and right lateral sleep positions at a 0.99 averaged precision and a 0.99 averaged recall. Given the amount of data accumulated per night and the instability in the signal quality, this fully automatic processing framework is indispensable for a personal healthcare system. Therefore, this study could serve as an important step for cECG technique trying to explore the cECG for unconstrained heart monitoring.
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Kido et al. (2019) studied Capacitive ECG signal quality and sleep position (n=12). CNN-based framework (qua_model and pos_model) was evaluated on Signal quality classification and sleep position recognition. A novel CNN-based framework for capacitive ECG achieved 0.99 precision and 0.99 recall for both signal quality classification and sleep position recognition.
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