Multilayer Perceptron Optimization of ECG Peaks for Cardiac Abnormality Detection

Abstract

The development of artificial neural networks (ANNs) was founded on computer alterations of human biology (the concept of neurons). The practicality of applying ANNs to various problems has been the subject of numerous studies, particularly in the field of biomedical engineering. Medical and educational decision-making regularly use applications to ANNs. Using a range of reference data, the ANNs used in the current study were trained to recognise cardiac abnormalities. Typically referred to as reference parameters, electrocardiogram (ECG) signal amplitude and duration are employed as input parameters for cardiac issues. An ECG complex consists of a P peak, QRS wave, and T peak. The amplitude and length of each P peak, QRS wave, and T peak are measured, resulting in a total of six input parameters for the artificial neural network. The artificial neural network (ANN) structure in this study is a multilayer perceptron (MLP), and the training techniques are Bayesian Regularization (BayR), Lavenberg Marquardt (LevM), and Backpropagation (BackP). The influence of the Tansig activation function on the MLP structure. The MLP network that achieved the highest accuracy (94.44%) utilising the BayR training method and Logsig activation function surpassed all others.