Multi-Head Attention Mechanism in CNN-LSTM for Real Time ECG Arrhythmia Detection

Heart arrhythmia detection is critical for diagnosing and managing cardiovascular diseases. Traditional methods for real-time ECG signal analysis, such as featurebased approaches and template matching, rely on manual feature extraction and rule-based classifiers. These methods often struggle with noisy data and fail to generalize across diverse arrhythmia types, limiting their accuracy and adaptability in real-world scenarios. To address these challenges, this study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from raw ECG signals, while the LSTM captures temporal dependencies, enabling robust and accurate arrhythmia detection in real-time settings. Experimental results on benchmark datasets demonstrate a 5% improvement in accuracy, F1-score, precision, and recall compared to traditional methods. The proposed system offers significant advantages, including scalability, noise resilience, and real-time processing capabilities, making it suitable for wearable health monitoring devices and telemedicine systems. These findings highlight the potential of deep learning to transform cardiac diagnostics.