ArticleSensors (Basel, Switzerland)2019
A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Design of Electrodes and Interfaces for Non-Contact and Capacitive Biomedical Measurements: Terminology, Electrical Model, and System Analysis.Sensors (Basel, Switzerland) · 2026Pooled it
- Machine Learning-Based Human Detection Using Active Non-Line-of-Sight Laser Sensing.Sensors (Basel, Switzerland) · 2026Article
- A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and Signal Quality Assessment.Sensors (Basel, Switzerland) · 2025Article
- A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals.Heliyon · 2024Article
- MCFN: A Multichannel Fusion Network for Sleep Apnea Syndrome Detection.Journal of healthcare engineering · 2023Article
- Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks.Biomedical engineering online · 2022Article
- Measuring Repositioning in Home Care for Pressure Injury Prevention and Management.Sensors (Basel, Switzerland) · 2022Article
- Robustness of electrocardiogram signal quality indices.Journal of the Royal Society, Interface · 2022Review
- Capacitively-Coupled ECG and Respiration for Sleep-Wake Prediction and Risk Detection in Sleep Apnea Patients.Sensors (Basel, Switzerland) · 2021Article
- Novel Stable Capacitive Electrocardiogram Measurement System.Sensors (Basel, Switzerland) · 2021Article
- Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair.PloS one · 2021Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.