Cardiac abnormalities from 12‐Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm

Abstract

SummaryCardiovascular disease (CVD) is a most dangerous disease in the world. Early accurate and automated identification helps the medical professional make a correct diagnosis and administer fast treatment and saving many lives. Several studies have been suggested in this area, but no one yield the expected outcomes owing to data imbalance issue in the medical and healthcare industries. To overcome this problem, a Deep Convolutional Neural Network Optimized with Nomadic People Optimization for Cardiac Abnormalities from 12‐Lead ECG Signals Prediction (CCA‐12L ECG‐DCNN‐NPO) is proposed in this manuscript. At first, the input data is pre‐processed under Morphological filtering and Extended Empirical wavelet transformation (MF‐EEWT) for removing the noise. Then one hot encoding technique is used to improve the predictions and classification accuracy of the method. Afterward, Residual Exemplars Local Binary Pattern (RELBP) based Feature extraction is used to extract the morphological and statistical features. These extracted features are given to DCNN classifier. It contains fully convolutional neural network (FCN) and encoder with decoder framework, which activates pixel‐wise categorization to exactly identify Cardiac abnormalities from 12‐Lead ECG signals. The visual geometry group network (VGGNet) is considered as a backbone of FCN for end‐to‐end training. Generally, DCNN method does not adopt any optimization modes to define the optimum parameters and to assure exact detection. Therefore, Nomadic People Optimization (NPO) is considered to enhance the DCNN weight parameters. The CCA‐12L ECG‐DCNN‐NPO technique is implemented in python and the efficacy is analyzed under performance metrics, such as sensitivity, precision, F‐Score, specificity, accuracy and error rate. From the analysis, the proposed technique attains higher accuracy 27.5%, 10.32%, and 16.65%, higher f‐score 30.93%, 11.14% and 15.3%, lower error rate 36.31%, 15.78%, and 28.08% compared with the existing methods, such as Detecting Cardiac Abnormalities from 12‐lead ECG Signals Under Feature Selection, Feature Extraction, and deep Learning Classification (CCA‐12L ECG‐RFC), Channel self‐attention deep learning framework for multi‐cardiac abnormality diagnosis from varied‐lead ECG signals (CCA‐12L ECG‐CSA‐DNN) and Cardiac disease categorization by electrocardiogram sensing utilizing deep neural network (CCA‐12L ECG‐DNN) respectively.