ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection

An electrocardiogram (ECG) computes the electrical functioning of the heart, which is mostly employed for finding various heart diseases of its feasibility and simplicity. Moreover, some of the abnormalities that exist in the heart can be determined through investigating the electrical signal of the heartbeat. In this paper, Exponential Political optimizer (EPO)-based Deep Quantum Neural Network (QNN) is developed to categorize the heartbeat for arrhythmia detection. Here, the ECG signal is pre-processing using the wandering path finding technique to abolish the baseline wandering. In addition, the arrhythmia detection is done with Deep QNN in which the weights of Deep QNN are trained using Exponential Political optimizer (EPO). The developed EPO algorithm is devised using the combination of Exponentially Weighted Moving Average (EWMA) and Political Optimizer (PO). Generally, deep learning techniques offer only the best output with high dimensional features such that the mined features are treated under data augmentation to increase the dimensionality of features. Furthermore, the experimentation of the developed scheme is attained the maximum sensitivity, accuracy, and specificity of 0.92, 0.914, and 0.917.