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Abstract

This research presents a novel deep learning framework for high-accuracy and time-sensitive heart attack detection, leveraging a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture. The framework integrates Electrocardiogram (ECG) signals and clinical data, optimized for minimal latency. Experimental results demonstrate superior performance compared to traditional methods, achieving significant improvements in accuracy, sensitivity, and time efficiency. Heart attacks, or myocardial infarctions, are a leading cause of death worldwide. Early detection is crucial for improving survival rates and reducing long-term complications. This paper presents a deep learning framework designed for high-accuracy, time-sensitive heart attack detection. The framework leverages a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze electrocardiogram (ECG) data. We evaluate the framework on a large dataset of ECG recordings, achieving an accuracy of 98.5% and a detection time of less than 10 seconds. The results demonstrate the potential of deep learning for real-time heart attack detection in clinical settings.