title: [Detection of PVCs with support vector machine].
abstract: The classifiction of heart beats is the foundation for automated arrhythmia monitoring devices. Support vector machnies (SVMs) have meant a great advance in solving classification or pattern recognition. This study describes SVM for the identification of premature ventricular contractions (PVCs) in surface ECGs. Features for the classification task are extracted by analyzing the heart rate, morphology and wavelet energy of the heart beats from a single lead. The performance of different SVMs is evaluated on the MIT-BIH arrhythmia database following the association for the advancement of medical instrumentation (AAMI) recommendations.
authors.affiliation: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China.
authors.forename: Li
authors.initials: L
authors.lastname: Shen
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D018879:Ventricular Premature Complexes / Q000145:classification / Q000175:diagnosis*