1. QRS detection of ECG signal using U-Net and DBSCAN
- Author
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Jinzhao Lin, Liu Ting, He Sijia, Gwanggil Jeon, Huiqian Wang, Kaining Han, Qinghui Liu, Junchao Wang, and Yu Pang
- Subjects
DBSCAN ,Computer Networks and Communications ,Noise (signal processing) ,Computer science ,business.industry ,Normalization (image processing) ,Pattern recognition ,QRS complex ,Hardware and Architecture ,Media Technology ,Spatial clustering ,Preprocessor ,Sensitivity (control systems) ,Artificial intelligence ,Ecg signal ,business ,Software - Abstract
QRS detection is a crucial task for ECG signal analysis, which is the preliminary and essential step to further recognition and diagnosis. This paper proposes a U-Net based method for QRS detection. The method consists of three steps including preprocessing, U-Net model, and density-based spatial clustering of applications with noise(DBSCAN). The normalization is carried out using the Z-score method in preprocessing. In this study, location prediction is conducted by the U-Net model. Subsequently, the U-Net outputs are thresholded and clustered by DBSCAN. Finally, the middle points of the cluster are regards as the R-peak of the QRS complex. We demonstrate that the proposed method achieving high accuracy on ECG signals from the MIT-BIH Arrhythmia Database(MITDB). The experimental results show an average sensitivity of 99.98 %, positive predictivity of 99.95 %, accuracy of 99.93 %, and F1-score of 99.97 %. Compared with other existing methods, the overall performance is comparable and even better in terms of accuracy and F1-score.
- Published
- 2021
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