Communications on Applied Mathematics and Computation ›› 2025, Vol. 7 ›› Issue (5): 2029-2042.doi: 10.1007/s42967-024-00467-x

• ORIGINAL PAPERS • 上一篇    

An ECG Segmentation Method Based on GMM and Clusterwise Regression

Min Li1, Raymond Chan2, Yumei Huang3, Tieyong Zeng4   

  1. 1. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, China;
    2. School of Data Science, Lingnan University, Tuen Mun, 999077, Hong Kong, China;
    3. School of Mathematics and Statistics, Center for Data Science, Lanzhou University, Lanzhou, 730000, Gansu, China;
    4. Faculty of Science, The Chinese University of Hong Kong, Shatin, 999077, Hong Kong, China
  • 收稿日期:2023-11-30 修回日期:2024-04-16 接受日期:2024-05-27 出版日期:2025-02-22 发布日期:2025-02-22
  • 通讯作者: Yumei Huang,E-mail:huangym@lzu.edu.cn E-mail:huangym@lzu.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (No. 11971215), the Science and Technology Project of Gansu Province of China (No. 22JR5RA391), Center for Data Science of Lanzhou University, China, and the Key Laboratory of Applied Mathematics and Complex Systems of Lanzhou University, China.

An ECG Segmentation Method Based on GMM and Clusterwise Regression

Min Li1, Raymond Chan2, Yumei Huang3, Tieyong Zeng4   

  1. 1. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, China;
    2. School of Data Science, Lingnan University, Tuen Mun, 999077, Hong Kong, China;
    3. School of Mathematics and Statistics, Center for Data Science, Lanzhou University, Lanzhou, 730000, Gansu, China;
    4. Faculty of Science, The Chinese University of Hong Kong, Shatin, 999077, Hong Kong, China
  • Received:2023-11-30 Revised:2024-04-16 Accepted:2024-05-27 Online:2025-02-22 Published:2025-02-22
  • Contact: Yumei Huang,E-mail:huangym@lzu.edu.cn E-mail:huangym@lzu.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (No. 11971215), the Science and Technology Project of Gansu Province of China (No. 22JR5RA391), Center for Data Science of Lanzhou University, China, and the Key Laboratory of Applied Mathematics and Complex Systems of Lanzhou University, China.

摘要: The electrocardiogram (ECG) segmentation needs to separate different waves from an ECG and cluster the waves simultaneously. Clusterwise regression is a useful approach that can segment and cluster the data simultaneously. In this paper, we apply the clusterwise regression method to segment the ECG. By modeling the ECG signal wave by the Gaussian mixture model (GMM) and introducing a weight function, we propose a minimization model that consists of the weighted sum of the negative log-likelihood and the total variation (TV) of the weight function. The TV of the weight function enforces the temporal consistency. A supervised algorithm is designed to solve the proposed model. Experimental results show the efficiency of the proposed method for the ECG segmentation.

关键词: Electrocardiogram (ECG), Segmentation, Fiducial point extraction, Gaussian mixture model (GMM), Clusterwise regression

Abstract: The electrocardiogram (ECG) segmentation needs to separate different waves from an ECG and cluster the waves simultaneously. Clusterwise regression is a useful approach that can segment and cluster the data simultaneously. In this paper, we apply the clusterwise regression method to segment the ECG. By modeling the ECG signal wave by the Gaussian mixture model (GMM) and introducing a weight function, we propose a minimization model that consists of the weighted sum of the negative log-likelihood and the total variation (TV) of the weight function. The TV of the weight function enforces the temporal consistency. A supervised algorithm is designed to solve the proposed model. Experimental results show the efficiency of the proposed method for the ECG segmentation.

Key words: Electrocardiogram (ECG), Segmentation, Fiducial point extraction, Gaussian mixture model (GMM), Clusterwise regression