Hebbian neural networks for principal component analysis provide high sensitivity and positive predictivity for automated ECG arrhythmia classification.
Supports automated ECG classification research; leaves open clinical adoption pending prospective validation.
In this paper, ECG arrhythmia classification using principal component analysis is proposed. Hebbian neural networks are used for computing the principal components of an ECG signal. This provides an unsupervised feature extraction, dimension reduction and an improved computing efficiency. Results from 14 pathological records obtained from the MIT ECG database demonstrate the capability of this method in differentiating between five different types of arrhythmia despite the variations in signal morphology. An average value for classification sensitivity and positive predictivity were found to be Se% = 98.1% and +P% = 94.7% respectively.
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Hasan Al‐Nashash (2000) studied this question.
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