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 simultaneousl...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 effi ciency of the proposed method for the EcG segmentation.展开更多
The aim of this paper is to develop an algorithm for solving the clusterwise linear least absolute deviations regression problem.This problem is formulated as a nonsmooth nonconvex optimization problem,and the objecti...The aim of this paper is to develop an algorithm for solving the clusterwise linear least absolute deviations regression problem.This problem is formulated as a nonsmooth nonconvex optimization problem,and the objective function is represented as a difference of convex functions.Optimality conditions are derived by using this representation.An algorithm is designed based on the difference of convex representation and an incremental approach.The proposed algorithm is tested using small to large artificial and real-world data sets.展开更多
基金National Natural Science Foundation of China(No.11971215)the Science and Technology Project of Gansu Province of China(No.22JR5RA391)+1 种基金Center for Data Science of Lanzhou University,Chinathe Key Laboratory of Applied Mathematics and Complex Systems of Lanzhou University,China.
文摘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 effi ciency of the proposed method for the EcG segmentation.
基金the Australian Research Council under Discovery Projects(No.DP140103213).
文摘The aim of this paper is to develop an algorithm for solving the clusterwise linear least absolute deviations regression problem.This problem is formulated as a nonsmooth nonconvex optimization problem,and the objective function is represented as a difference of convex functions.Optimality conditions are derived by using this representation.An algorithm is designed based on the difference of convex representation and an incremental approach.The proposed algorithm is tested using small to large artificial and real-world data sets.
