Key result
A hybrid system based on the artificial bee colony algorithm and a multilayer perceptron neural network achieved high classification accuracy for detecting arrhythmia from ECG signals.
Why the study?
The high mortality from cardiovascular diseases motivates the development of accurate methods for arrhythmia detection from ECG signals.
Novel ECG segmentation metrics extend signal-processing tools; leaves open clinical validation before diagnostic use.
The number of people who die due to cardiovascular diseases is quite high. In our study, ECG (electrocar-diogram) signals were divided into segments and waves based on temporal boundaries. Signal similarity methods such as convolution, correlation, covariance, signal peak to noise ratio (PNRS), structural similarity index (SSIM), one of the basic statistical parameters, arithmetic mean and entropy were applied to each of these sections. In addition, a square error-based new approach was applied and the difference of the signs from the mean sign was taken and used as a feature vector. The obtained feature vectors are used in the artificial bee colony algorithm; whose fitness function is a multilayer perceptron neural network. It has been shown that the hybrid system we have developed achieves high classification accuracy by selecting the most appropriate parameters for the detection of arrhythmia from ECG signals.
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Ersoy et al. (2022) studied Arrhythmia. Hybrid system based on Artificial Bee Colony (ABC) algorithm and multilayer perceptron neural network was evaluated on Detection of arrhythmia from ECG signals. A hybrid system based on the artificial bee colony algorithm and a multilayer perceptron neural network achieved high classification accuracy for detecting arrhythmia from ECG signals.
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