The proposed FSVM_n classifier demonstrates high accuracy in classifying ECG arrhythmia patterns, outperforming existing SVM and FSVM algorithms.
Promising for automated ECG arrhythmia detection; extends SVM methods but remains hypothesis-generating pending clinical validation.
This paper proposes a fuzzy support vector machine (<TEX>FSVMₙ</TEX>) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The <TEX>FSVMₙ</TEX> is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the <TEX>FSVMₙ</TEX> shows better pattern classification performance than the existing SVM and FSVM algorithms.
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Lee et al. (2011) studied this question.
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