A new method of detecting the characteristic waves and their onset and end in electrocardiogram signals. (May 2022) - Li, Guixiang; Huang, Dequn; Wang, Lei; Zhou, Jing; Chen, Jun; Wu, Kai; Xu, Weikang
Highlights: The proposed method is an ECG characteristic wave and their onset and end detector. WT combined with arithmetic operation enriches the energy of characteristic waves. Zone slope maximization method detects the onset and end of a characteristic wave. The method shows better performance in P-wave detection compared with some others. The computation time of detection is not longer than 5.77 s on 15 min ECG signal. Abstract: Electrocardiogram (ECG) diagnosis clinical mainly depends on the amplitude, shape, and duration of the characteristic waves (CW). However, they are often difficult to obtain for the acquisition process is vulnerable to interference, and ECG has many types and variations. This study proposed a new method of detecting the CW and their onset and end of ECG. It includes three stages. First, the wavelet transform (WT) was used to filter the signal. Second, the WT combined arithmetic operation, adaptive threshold within a fixed window was applied to detect the CW. Third, a zone slope maximization method was adopted to detect the onset and end of CW based on the peak. During the last two stages, a search window and threshold correction was performed. The sensitivity (Se), positive predictivity (PP), accuracy (Acc), and computation time (CT) were validated via data from MIT-BIH arrhythmia database and QT database. Se%, PP% and Acc% of values >= 98.64, 98.64, 97.30, and 98.82, 98.82, 97.67 were obtained for the peak of CW detection, those values >= 98.48, 98.48, 97.00, and 98.74, 98.74, 97.51 for the onset and end detection, respectively. CT <= 5.77 s on 15 min ECG. The proposed method extracted the CW and their onset and ends with good performance, especially for P-wave. Those features are further applied to obtain the ECG diagnosis clinical basis such as heart rate, amplitude, duration. It would be a new method for extracting the diagnosis basis of heart disease and provide a foundation for developing intelligent recognition systems.