Abstract
Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.
Highlights
Unobtrusive monitoring of physiological signals is a field of research that has seen an increased interest during the last decades, due to its potential to enable a patient-centered healthcare with lower costs, higher population screening and an increased patient/user comfort
As a result of the visual quality levels Visual Quality (VQ) assigned to each segment, and in order to evaluate the overall quality of capacitively-coupled electrocardiography (ccECG) across subjects, Table 3 is presented with the quality distribution obtained when including the best segment from each measurement
This study presented, for the first time, the evaluation of a multi-channel ccECG system for the extraction of ECG features previously demonstrated to be highly effective in the automatic detection of sleep apnea
Summary
Unobtrusive monitoring of physiological signals is a field of research that has seen an increased interest during the last decades, due to its potential to enable a patient-centered healthcare with lower costs, higher population screening and an increased patient/user comfort. Through-clothing ccECG measurements have been demonstrated to have high similarity to medical-grade electrocardiographic (ECG) recordings when using strapped bands [2,4] and even when integrated in everyday life objects [5,6], with a correlation of up to 90% [6] This high-quality ccECG is only obtained during certain periods of the unobtrusive monitoring; it is essential to be able to discriminate high-quality signals that can be used for health screening from artefact-contaminated ccECG only usable for a limited analysis, and even signals not useful at all due to high noise content or saturation resulting from poor electrode-body coupling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
