Hybrid LSTM-Attention Framework for Enhanced Classification of Cardiac Abnormalities from ECG Signals

Early detection of arrhythmias is crucial for preventing severe cardiac events and improving patient outcomes. Traditional ECG classification methods struggle to capture complex temporal and spatial patterns in the data. While deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks show promise, they often face trade-offs between accuracy and interpretability. CNNs excel in feature extraction, and LSTMs are effective for modeling temporal sequences, but many existing models fail to capture long- range dependencies or provide clinical transparency. Exploring hybrid approaches that combine these techniques with attention mechanisms to focus on critical ECG segments remains an untapped opportunity. This paper proposes an innovative CNN-LSTM-Attention framework, which integrates the use of CNNs to extract features from ECGs automatically, LSTMs to model the sequence pattern, and self-attention on top to increase focus around critical segments of ECG signals. Besides its improvement over classification performance, the added interpretability into the working of the model opens up novel avenues for analysis. Our proposed model gives state-of-the-art performance with accuracy at 91.4%, AUC-ROC of 0.95, and other competitive metrics for precision, recall, and sensitivity on publicly available ECG datasets. Detection of a broad range of arrhythmias while at the same time providing necessary transparency makes this model perfect for real-world clinical practice. The paper gives an important idea toward developing real-time automation in arrhythmia detection with enhanced accuracy and interpretability. The proposed CNN-LSTM-Attention model breaks new ground in ECG classification, achieving high performance as well as clinical reliability and paving the way for further improvements in intelligent healthcare systems.