Integrating Deep Learning for Comprehensive Detection and Optimization of ECG-Based Arrhythmia Using Gaussian Function Model

The rapid advancement in deep learning has opened new avenues for the accurate detection and classification of ar-rhythmias using electrocardiogram (ECG) signals. This study fo-cuses on integrating deep learning techniques with Gaussian function models to enhance the detection of ECG-based arrhythmias. Traditional methods of arrhythmia detection often face challenges due to the complex and varied nature of ECG signals. By employing Gaussian function models, this work aims to create a more generalized representation of ECG beats, which can be effectively utilized by deep learning algorithms. The proposed approach combines the strengths of Gaussian modeling with advanced deep learning techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Channel Attention Mechanism, and Transformer Encoders. These components collectively improve feature extraction, enhance contextual understanding, and refine the classification process, leading to a comprehensive and robust detection mechanism for various types of arrhythmias. Experimental results demonstrate that the integrated model significantly outperforms existing methods in terms of accuracy and computational efficiency, providing a promising tool for real-time cardiac monitoring and early diagnosis of heart conditions.