Post-processing refined ECG delineation based on 1D-UNet

Abstract

The Electrocardiography (ECG) serves as a standard method for diagnosing cardiovascular disease due to its minimal risk, affordable price and simple application. Clinical information is embedded implicitly in the intervals, amplitude or morphology of different waves that represent the heart-beat cycle in the form of ECG signals. Manually delineating the ECG signals to locate the boundaries of the P, QRS, and T waves in each heart-beat typically requires a long time professional training. To facilitate the delineation and to increase precision, we propose a post-processing refined ECG delineation method that takes full advantage of the morphological information of a heartbeat cycle both in classifying each sample point in the ECG recording using 1D-UNet and determining the boundaries of these waveforms. To proceed, the ECG signals are split into pieces of only one heart beat cycle before sending into the 1D-UNet. These single beat-annotated ECG segments enable the network better extract the local and global features of different waveforms in the ECG, which gives rise to a very precise categorization of each sample point. The post-processing algorithm then uses the morphological information of ECG signal to get rid of the influence of the misclassified data points on the extraction of the onset/offset of different wave components. Tests carried on the two public ECG databases, i.e., the LUDB and QTDB show satisfactory delineation performance, with sensitivities 99.88% and 99.48%, respectively. These results suggest possible applications in wearable and wireless devices for health monitoring.