An Improved Sliding Window Area Method for T Wave Detection.

Autor: Shang H; School of Control Science and Engineering, Shandong University, Jinan 250061, China., Wei S; School of Control Science and Engineering, Shandong University, Jinan 250061, China., Liu F; School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China., Wei D; Department of Electronic & Electrical Engineering, Bath University, Bath BA27AY, UK., Chen L; School of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250355, China., Liu C; School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Jazyk: angličtina
Zdroj: Computational and mathematical methods in medicine [Comput Math Methods Med] 2019 Apr 01; Vol. 2019, pp. 3130527. Date of Electronic Publication: 2019 Apr 01 (Print Publication: 2019).
DOI: 10.1155/2019/3130527
Abstrakt: Background: The T wave represents ECG repolarization, whose detection is required during myocardial ischemia, and the first significant change in the ECG signal is being observed in the ST segment followed by changes in other waves like P wave and QRS complex. To offer guidance in clinical diagnosis, decision-making, and daily mobile ECG monitoring, the T wave needs to be detected firstly. Recently, the sliding area-based method has received an increasing amount of attention due to its robustness and low computational burden. However, the parameter setting of the search window's boundaries in this method is not adaptive. Therefore, in this study, we proposed an improved sliding window area method with more adaptive parameter setting for T wave detection.
Methods: Firstly, k -means clustering was used in the annotated MIT QT database to generate three piecewise functions for delineating the relationship between the RR interval and the interval from the R peak to the T wave onset and that between the RR interval and the interval from the R peak to the T wave offset. Then, the grid search technique combined with 5-fold cross validation was used to select the suitable parameters' combination for the sliding window area method.
Results: With respect to onset detection in the QT database, F 1 improved from 54.70% to 70.46% and 54.05% to 72.94% for the first and second electrocardiogram (ECG) channels, respectively. For offset detection, F 1 also improved in both channels as it did in the European ST-T database.
Conclusions: F 1 results from the improved algorithm version were higher than those from the traditional method, indicating a potentially useful application for the proposed method in ECG monitoring.
Databáze: MEDLINE
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