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.
ECG, artificial bee colony, artificial neural network, machine learning, optimization, feature selection, arrhythmia
ERSOY, ERSİN; BOSTANCI, GAZİ ERKAN; and GÜZEL, MEHMET SERDAR
"Development of a hybrid system based on ABC algorithm for selection of appropriate parameters for disease diagnosis from ECG signals,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
5, Article 8.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss5/8