ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
Expert Systems with Applications: An International Journal
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The ventricular arrhythmias including ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening heart diseases. This paper presents a novel method for detecting normal sinus rhythm (NSR), VF, and VT from the MIT/BIH Malignant Ventricular Arrhythmia Database using the neural network with weighted fuzzy membership functions (NEWFM). This paper separates pre-processing into 2 steps. In the first step, ECG beasts are transformed by using Filtering Function [1]. In the second step, transformed ECG beasts produce 240 numbers of probability density curves and 100 points in each probability density curve using the probability density function (PDF) processing. By using three statistical methods, 19 features can be generated from these 100 points of probability density curve, which are the input data of NEWFM. The 15 generalized features from 19 PDF features are selected by non-overlap area measurement method [4]. The BSWFMs of the 15 features trained by NEWFM are shown visually. Since each BSWFM combines multiple weighted fuzzy membership functions into one using bounded sum, the 15 small-sized BSWFMs can realize NSR, VF, and VT detection in mobile environment. The accuracy rates of NSR, VF, and VT is 98.75%, 76.25%, and 63.75%, respectively.