title: A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression.
abstract: Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.
authors.affiliation: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
authors.forename: Jianli
authors.initials: J
authors.lastname: Yang
authors.affiliation: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Bai
authors.affiliation: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
authors.forename: Guojun
authors.initials: G
authors.lastname: Li
authors.affiliation: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Liu
authors.affiliation: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
authors.forename: Xiuling
authors.initials: X
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D018880:Atrial Premature Complexes / Q000175:diagnosis; D002037:Bundle-Branch Block / Q000175:diagnosis; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018879:Ventricular Premature Complexes / Q000175:diagnosis*
keywords: PVC diagnosis; feature extraction; softmax regression; sparse auto-encoder