title: Sparse representation-based heartbeat classification using independent component analysis.
abstract: The classification of heartbeats is crucial to identify an arrhythmia. This paper proposes a new method that combines independent component analysis (ICA) with sparse representation-based classification (SRC) to distinguish eight types of heartbeats. We use ICA to extract useful features from heartbeats. A feature vector consists of 100 ICA features along with a RR interval. We use SRC to compute a sparse representation of a test feature vector with respect to all training feature vectors. The type of a test feature vector is determined using the concentration degree of sparse coefficients on each heartbeat type. For experimental purposes, 9800 heartbeats are extracted from the MIT-BIH electrocardiogram (ECG) database. The results show that our proposed method performs better than conventional methods, with 98.35% accuracy and 94.49%-100% sensitivities to several heartbeat types.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing, China. hfhuang@bjtu.edu.cn
authors.forename: Hui Fang
authors.initials: HF
authors.lastname: Huang
authors.forename: Guang Shu
authors.initials: GS
authors.lastname: Hu
authors.forename: Li
authors.initials: L
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification* / Q000175:diagnosis; D004562:Electrocardiography; D006347:Heart Sounds / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted