Arrhythmia Detection Using Convolutional Neural Network

Abstract

This study introduces a novel approach for arrhythmia detection utilizing Electrocardiogram (ECG) signals, through a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture. This method leverages the CNN's capacity for intricate feature extraction combined with the LSTM's ability to analyze time-series data, optimizing for the complexities inherent in ECG analysis. Our customized model integrates advanced convolutional layers and LSTM units to enhance precision in detecting arrhythmic events and understanding cardiac rhythms over time. A key aspect of our methodology is the application of advanced data augmentation techniques. These strategies are instrumental in enriching the training dataset, allowing the model to better generalize across varied and unseen ECG signals, thereby enhancing its overall detection accuracy. This research marks a significant leap forward in the realm of medical diagnostics, providing a highly accurate, non-invasive diagnostic tool for arrhythmia detection. By combining the strengths of CNNs and LSTMs, we illustrate the potential of deep learning in addressing the nuanced challenges of arrhythmia detection, setting a new benchmark for innovation in automated cardiac monitoring and care. Incorporating a comprehensive pre-processing pipeline and sophisticated data augmentation techniques, the model is designed to accurately normalize and transform ECG signals, facilitating improved feature identification and model generalization across diverse ECG patterns. This research represents a significant advancement in medical diagnostics, offering a highly accurate and non-invasive tool for cardiac monitoring. By merging CNN and LSTM capabilities, we demonstrate the potential of deep learning for nuanced arrhythmia detection, paving the way for future innovations in automated cardiac care.