Arrhythmia classification using ECG signal: A meta-heuristic improvement of optimal weighted feature integration and attention-based hybrid deep learning model

Abstract

In healthcare facilities, the most common and least expensive diagnostic tool for monitoring electrical signals in the heart is the Electrocardiogram (ECG). Arrhythmia is nothing but abnormal rhythms in the heart. Arrhythmias can be dangerous and fatal. An ECG test can identify the type of arrhythmia that's causing the abnormal heartbeat. These necessitate lengthy, high-quality ECG segments, which may result in a few important episode omissions. Because of this, a low-cost, dependable, and automatic system for screening and treating arrhythmia becomes highly necessary. A novel method for the classification of arrhythmias is suggested on the basis of attention-based deep learning to satisfy the above-mentioned requirement. Signal collection, signal analysis, feature extraction, weighted fused features, and classification are the various phases of the proposed work. The input signal is initially obtained from benchmark data sources. The RR interval and QRS complex signal analysis are carried out next. A One-Dimensional Convolution Neural Network (1DCNN) is used to extract the deep features from the resulting RR interval and QRS complex, as well as the Higher-Order Statistical (HOS) features from the raw signal. The weighted fused feature selection is carried out after the above-mentioned three features have been obtained, where the weight is adjusted with the Recommended Adaptive Risk Rate-based Lemurs Optimization Algorithm (ARR-LO). Finally, the fused features are fed into the hybrid Attention model through the Long Short-Term Memory (LSTM) and Deep Temporal Convolution Network (DTCN), where ARR-LO is used to optimally determine some of the hyperparameters. The objectives of the study include developing an arrhythmia classification framework, constructing an adaptive attention-based hybrid deep learning model (HA-LSTM-DTCN), creating a new meta-heuristic algorithm (ARR-LO), and validating the suggested model against existing classifiers. The performance is compared with the conventional models and evaluated using a variety of metrics and the results from the simulation, the generated deep learning-based arrhythmia classification framework outperformed conventional prediction models such as DTCN, LSTM-DTCN, DNN, and LSTM classifiers in classifying the arrhythmia by 81.79%, 55.46%, and 80.73%, respectively and the overall accuracy of the proposed framework performs 94.425%. From the results, it was shown that generated AR-LO-HA-LSTM-DTCN model for arrhythmia classification improved the classification performance, thus making it useful in the quick diagnosis of arrhythmia disease. The findings show that the suggested arrhythmia classification model improves the classification performance, which can be used to quickly diagnose arrhythmia disease.