title: Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots.
abstract: The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system.
authors.affiliation: The First Affiliation Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, People's Republic of China.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Zhang
authors.forename: Tianci
authors.initials: T
authors.lastname: Guo
authors.forename: Bin
authors.initials: B
authors.lastname: Xi
authors.forename: Yang
authors.initials: Y
authors.lastname: Fan
authors.forename: Kun
authors.initials: K
authors.lastname: Wang
authors.forename: Jiacheng
authors.initials: J
authors.lastname: Bi
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000503:physiopathology*; D001331:Automation; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D046709:Pattern Recognition, Physiological; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine