Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea.

A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients.

On the PSG data, an AUC of 76.3% was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0%. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4%.

This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.