ECG signal classification using CNN & LSTM with Aquila Optimization technique

Abstract

In this research article, the development of ECG signal classification using CNN_LSTM with Aquila Optimization technique is presented along with the simulation results. The research study titled "ECG Signal Classification Using CNN & LSTM with Aquila Optimization Technique" presents a novel approach for the accurate and efficient classification of electrocardiogram (ECG) signals. In this study, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are employed as deep learning architectures to extract relevant features and capture temporal dependencies in ECG waveforms. To enhance the model's performance and convergence, the Aquila Optimization Technique is introduced, a customized optimization algorithm designed to address the unique challenges of ECG signal classification. The proposed methodology is evaluated on a diverse and comprehensive ECG dataset, and its performance is compared with existing classification methods. The results demonstrate the effectiveness of the CNN and LSTM combination, augmented by the Aquila Optimization Technique, in achieving high classification accuracy and robustness for various cardiac arrhythmia classes. This research has significant implications for the field of medical diagnostics, as it provides a promising avenue for accurate and real-time ECG signal analysis, ultimately contributing to the early detection and treatment of cardiovascular diseases.