Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

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.

Data Quality Assessment of Capacitively-coupled ECG Signals

“…ccECG signals from diverse scenarios were used. These signals included data recorded from a system described in [5,10] as well as from the publicly available UnoVis dataset [11]. The data comprised 10000 randomly selected ccECG segments of 15 seconds, resulting in the distribution shown in Table 1.…”

“…48 SQI features were extracted from each ccECG segment, including the features evaluated in [10]. Feature selection (FS) was performed on the training set by means of: 1. neigborhood component analysis (NCA) [12] available in the machine learning toolbox of Matlab ® ; 2.…”

Sistema simples com eletrodos capacitivos para monitoramento da frequência cardíaca através de contato indireto

“…A correlação se mostra como um bom parâmetro para quantificar o grau de semelhança de sinais, sendo utilizada na literatura para este fim 16,20,22,23 .…”

Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors

“…This uses a classifier described in more detail in [28]; 2. The identification of artefacts using a template-based signal quality metric previously evaluated by the authors [29], in combination with an amplitude-based metric calculated as the percentage of 0.5-s sub-windows that have data within [0.05-3] mV range; 3. The selection of the channel.…”