This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhythmias from electrocardiogram (ECG) signals. The proposed architecture leverages the complementary strengths of both components: the CNN layers autonomously learn and extract salient morphological features from raw ECG waveforms, while the BLSTM layers effectively model the sequential and temporal dependencies inherent in ECG signals, thereby improving diagnostic accuracy. To further enhance training stability and non-linear representation capability, the Mish activation function is incorporated throughout the network. The model was trained and evaluated using a combination of the widely recognized MIT-BIH Arrhythmia Database and de-identified clinical ECG recordings sourced from collaborating healthcare institutions, ensuring both diversity and clinical relevance of the dataset. Notably, the framework operates with minimal preprocessing, underscoring its practical viability for real-time implementation. Experimental results demonstrate the model’s exceptional performance, achieving an overall classification accuracy of 99.52%, sensitivity of 99.48%, and specificity of 99.85%. These outcomes highlight the model’s robustness, generalizability, and strong potential for integration into clinical decision support systems, particularly in high-throughput or resource-constrained healthcare environments.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, -means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.