Key result
An adaptive neuro-fuzzy network classified heart abnormalities in 10 different cardiac states with an accuracy level of more than 94%.
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An adaptive neuro-fuzzy network can effectively automate the classification of heart abnormalities from heart rate signals with high accuracy.
Analysis of heart rate has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). These signals may either contain indicators of a current disease or even warnings about impending diseases. However, to manually study and pinpoint heart abnormalities in voluminous data is strenuous and time consuming. Here, an adaptive neuro-fuzzy network is used to classify heart abnormalities in 10 different cardiac states and shown to be effective. The results indicate a high level of efficacy of tools used with an accuracy level of more than 94%.
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Kannathal et al. (2006) studied Heart abnormalities. Adaptive neuro-fuzzy network was evaluated on Classification accuracy of heart abnormalities in 10 different cardiac states. An adaptive neuro-fuzzy network classified heart abnormalities in 10 different cardiac states with an accuracy level of more than 94%.
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