An intelligent deep neural network with Opposition based Laplacian Equilibrium Optimizer to improve feature extraction using ECG signals

Abstract

ProblemThe leading cause of death in the world is cardiovascular disease (CVD). It is required to detect, prevent and manage cardiovascular diseases to reduce their effect and improve the outcome. Interpretation of ECG signals may vary from one expert to another. That’s why an automatic categorization of ECG recordings is needed. AimThe proposed study attempted to address the above issues by developing an automated method for predicting arrhythmias based on ECG signal categorization. This method would offer more precise and enhanced interpretation of ECGs while reducing small biases brought on by manual interpretation. MethodsIn this paper, a unique deep learning-based algorithm is used to efficiently and swiftly classify cardiac arrhythmias. We have used Discrete Wavelet Transformation (DWT) to appropriately classify ECG data into important arrhythmia categories during the pre-processing stage by removing intrinsic noise from ECG signals. To obtain optimal placement (equilibrium state) for better search space diversification, OB-L-EO uses Laplace distribution followed by opposition-based modelling to modify the intensity of search agents. A powerful type of machine learning technique called deep neural network (DNN) is used to accomplish signal categorization among sixteen categories using the MIT-BIH Arrhythmia ECG dataset. ResultsWith the help of analysing classification technique including optimized feature vector and the OBLEO method with DNN, the heart rate has been detected with a 98.779% accuracy, 98.67% sensitivity, 98.87% specificity and execution time with a value of 1.34 ms across the entire dataset. The application of 10-fold cross validation provides the better accuracy i.e 99.79%. ConclusionThe achieved performance parameters have demonstrated that the suggested ECG signal classification model is capable of categorizing arrhythmia classes appropriately using ECG signals.