J Med Syst - Sparse representation-based heartbeat classification using independent component analysis.

Tópicos

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Resumo

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.

Resumo Limpo

classif heartbeat crucial identifi arrhythmia paper propos new method combin independ compon analysi ica spars representationbas classif src distinguish eight type heartbeat use ica extract use featur heartbeat featur vector consist ica featur along rr interv use src comput spars represent test featur vector respect train featur vector type test featur vector determin use concentr degre spars coeffici heartbeat type experiment purpos heartbeat extract mitbih electrocardiogram ecg databas result show propos method perform better convent method accuraci sensit sever heartbeat type

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