Unobtrusive, Through-Clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

Ivan Castro*, Aakash Patel, Margot Deviaene, Dorien Huysmans, Pascal Borzee, Bertien Buyse, Dries Testelmans, Sabine Van Huffel, Carolina Varon, Tom Torfs

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
14 Downloads (Pure)


A real-life validation of a system for simultaneous acquisition of capacitively-coupled ECG (ccECG) and capacitively-coupled bioimpedance (ccBioz) is presented. The heart rate (HR) and respiration rate (RR) estimation performance was evaluated using polysomnography (PSG) signals as ground-truth, in recordings from 28 patients with suspected obstructive sleep apnea (OSA). A ccECG beat detection sensitivity of 98.4% and an R-R interval mean absolute error (MAE) of 17.1 ms were achieved when applying quality-based algorithms. RR MAE values of 3.48 and 6.37 breaths per minute were also achieved when using two different RR extraction methods. High similarity between unobtrusive signals and PSG ground-truth was observed, with a correlation between ccECG and psgECG of 91.5% and a correlation between ccBioz and PSG thoracic belt (TB) of 89.5%. Even in episodes containing OSA events, the characteristic respiration behavior of TB signals was also observed in the ccBioz signals. This shows the potential of ccECG and ccBioz for use in long-term monitoring without adding discomfort to the patient or user. Sleep-related applications as well as more generic cardiorespiratory monitoring in (patient) beds are obvious applications, but also other daily life monitoring can be done using a similar approach (e.g. in seats).

Original languageEnglish
Title of host publication2020 Computing in Cardiology, CinC 2020
Number of pages4
ISBN (Electronic)9781728173825
Publication statusPublished - 2020
Event2020 Computing in Cardiology, CinC 2020 - Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference2020 Computing in Cardiology, CinC 2020


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