Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis |
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Authors: | Hui Fang Huang Guang Shu Hu Li Zhu |
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Institution: | (1) Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China;(2) Department of Biomedical Engineering, Beijing Jiaotong University, Beijing, 100044, China |
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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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Keywords: | |
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