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).

Sleep apnea is a common sleep disorder, whose diagnosis can strongly benefit from home-based screening. As the total sleep time is essential to assess the sleep apnea severity, a sleep-wake classifier was developed based on heart rate and respiration. These two signals were selected as they can be measured using unobtrusive sensors. A 1D convolutional neural network (CNN) was designed to classify 30s epochs of tachograms and respiratory inductance plethysmography (RIP) signals. The input based on beat-to-beat variability allows the use of different sensor types. A dataset of 56 patients with an apnea-hypopnea index (AHI) below 10 was used to train and validate the network. This CNN was applied to an independent test set of ECG and RIP signals of 25 subjects. Of these, 8 subjects were simultaneously monitored using an unobtrusive capacitive-coupled ECG (ccECG) sensor integrated in a mattress. Artefact removal and data correction was performed on this acquired data. The performance on the independent dataset of ECG and RIP is comparable to state-of-the-art, with ? = 0.48. However, application on the ccECG data resulted in a drop in performance, with ? = 0.30. This was caused by a low amount of remaining wake epochs after data cleaning. Importantly, the network classified 30s segments of sleep apnea patients, without relying on past or future information for feature extraction.