A neural network with weighted fuzzy membership functions (NEWFM) achieved reliable PVC detection accuracy rates of 99.80%, 99.21%, and 98.78% across three datasets from the MIT-BIH database.
Does a fuzzy neural network system accurately detect premature ventricular contractions in ECG data?
A fuzzy neural network system using eight wavelet-transformed coefficients can detect premature ventricular contractions with high accuracy (>98%), potentially enabling real-time detection in mobile environments.
Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using wavelet transformed coefficients from the MIT-BIH PVC database. The eight generalized coefficients, locally related to the time signal, are extracted by the nonoverlap area distribution measurement method. The eight generalized coefficients are used for the three PVC data sets with reliable accuracy rates of 99.80%, 99.21%, and 98.78%, respectively, which means that the selected features are less dependent on the data sets. It is shown that the locations of the eight features are not only around the QRS complex that represents ventricular depolarization in the electrocardiogram (ECG) containing a Q wave, an R wave, and an S wave, but also the QR segment from the Q wave to the R wave has more discriminate information than the RS segment from the R wave to the S wave. The BSWFMs of the eight features trained by NEWFM are shown visually, which makes the features explicitly interpretable. Since each BSWFM combines multiple weighted fuzzy membership functions into one using the bounded sum, the eight small-sized BSWFMs can realize real-time PVC detection in a mobile environment.
Joon S. Lim (Fri,) conducted a other in Premature ventricular contractions (PVCs). Neural network with weighted fuzzy membership functions (NEWFM) was evaluated on PVC detection accuracy. A neural network with weighted fuzzy membership functions (NEWFM) achieved reliable PVC detection accuracy rates of 99.80%, 99.21%, and 98.78% across three datasets from the MIT-BIH database.