Respiration Extraction from Single-Channel ECG using Signal-Processing Methods and Deep Learning

The measured bio-potential on the surface of the body can give most of the information on the health status of an individual. Sensors that measure the ECG potential difference on the body surface could also provide information on other vital functions indirectly, like respiration, by a customized analysis of the ECG signal. Respiration is one of the most characteristic vital signs and can reflect the status of a patient or the progression of an illness. In this paper, we utilize signal-processing and deep learning methods for the extraction of the respiratory signal from the differential surface potential of a single-channel ECG. From signal processing, we investigate feature-based and filter-based methods, while from deep learning, an encoder-decoder architecture. Simultaneous measurements of a single-channel ECG and respiration have been obtained from 61 subjects before and after cardiac intervention in several positions of the body. We also investigate the power of the methods for respiration extraction depending on the period (pre-/post-operation) and the position of the body when the signal is obtained. The results show that the deep learning approach performs better than the filter-based methods but worse than the feature-based. Moreover, we conclude that different body positions do not influence respiration extraction significantly before and after the operation.