Optimal fused feature selection with ensemble learning foratrial fibrillation detection using ECG with enhanced average and subtraction-based optimizer

Most common asymptomatic arrhythmia that significantly leads to death and morbidity is Atrial Fibrillation (AF). It has the ability to extract valuable features is necessary for AF identification. Still, many existing studies have relied on weak frequencies that, are Time-Frequency Energy (TFE) and shallow time features. It requires lengthy ECG data to confine the information and is unable to confine the slight variation affected by the previous AF. The interfering noise signals focus primarily on separating AF from signals with a Sinus Rhythm (SR). Thus, this study would explore the detection of AF with heuristic-assisted deep learning approaches. Initially, the ECG Signals are gathered from the standard resources. Next, these gathered signals are pre-processed to perform denoising and artifact removal for enhancing the quality of data for further processes. Then, the deep feature extraction is done in two phases. In the first phase, the RR interval is extracted from the pre-processing ECG signals and the deep features are removed utilizing a Convolutional Neural Network (CNN). In contrast, deep features are employed to extract the features from the pre-processed ECG signals using the same CNN in the second phase. Then, these gathered in-depth features are fused and fed to the newly suggested heuristic algorithm called Enhanced Average and Subtraction-Based Optimizer (E-ASBO) for selecting the optimal fused features for reducing the redundancy in the signals. Finally, the chosen optimal fused features are fed to the new Adaptive Ensemble Neural Network (AENN) with heuristic adoption with the techniques such as Elma Neural Network, Deep Neural Network (DNN), and Recurrent Neural Network (RNN). This model focuses on increasing the accuracy of detecting AF. These proposed networks have more significant potential in future AF screening or clinical computer-aided AF diagnosis in wearable devices. It has superior performance and intuitive nature compared to the existing works.