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.
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Huang, H.F., Hu, G.S. & Zhu, L. Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis. J Med Syst 36, 1235–1247 (2012). https://doi.org/10.1007/s10916-010-9585-x
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DOI: https://doi.org/10.1007/s10916-010-9585-x