Post-processing refined ECG delineation based on 1D-UNet. (January 2023)

• Record Type: Journal Article
• Authors: Chen, Zhenqin; Wang, Mengying; Zhang, Meiyu; Huang, Wei; Gu, Hanjie; Xu, Jinshan
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Electrocardiography (ECG) -- Delineation -- 1D-UNet -- Heat-beat cycle; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104106
• Languages: English
• ISSNs: 1746-8094

Abstract

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. Highlights: We highlight the scope of the ECG delineation that the paper focuses and the main technique contributions to the delineation problem in the abstract. We illustrate the theoretical basis for applying the UNet network in tackling the ECG delineation task, and compare its performance with that of the other deep neural network-based solutions. The process of segmenting ECG signals into pieces of single heartbeat segments is detailed. Its contributions to the sample point classification and the delineation are explained. The theoretical basis for the post-processing algorithm is explained. New experiments are carried out to demonstrate the effectiveness of the proposed method. A careful proof-reading is done to correct the grammar errors, typos and vague expressions.