An adaptive Marine Predator Optimization Algorithm (MPOA) integrated Gated Recurrent Neural Network (GRNN) classifier model for arrhythmia detection

Abstract

Cardiovascular disorders are typically diagnosed using an Electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart's muscles. By monitoring the electrical activity of the heart, an ECG can be used to identify irregular heartbeats, strokes, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify heart problems have been conducted during the past few years. The disadvantages of the current and hybrid approaches include low accuracy, prolonged time, and a dearth of high-quality datasets. The proposed work aims to design a new detection tool for the diagnosis of Arrhythmia from ECG signals. In this study, the wavelet transformation technique is used for preprocessing after collecting the input ECG signal in order to create the quality-improved and normalized signals. The most pertinent characteristics are then selected from the preprocessed output using the cutting-edge Marine Predator Optimization Algorithm (MPOA), which accelerates classifier learning. In addition, the hyper tuned Gated Recurrent Neural Network (Hyp-GRNN) method is used to learn the most effective features and properly classify the normal and arrhythmia-affected signals. The Ebola Optimization Algorithm (EOSA) is used to execute the hyper parameter tweaking in order to enhance the accuracy and training time of the classifier. At the time of evaluation, performance is assessed and compared using a variety of metrics, including accuracy, precision, recall, F-measure and etc.