Improving Heart Sound Diagnosis with a Combined CNN-LSTM and Dual-Attention Deep Learning Model

Accurate classification of heart sounds is critical for the early detection and diagnosis of cardiovascular diseases. This research presents an automated technique for classifying heart sounds into normal, murmur, and extrasystolic categories. The approach initiates with a bandpass filtering preprocessing phase, aimed at improving the quality of heart sound recordings and minimizing noise by preserving pertinent frequencies between 20 Hz and 150 Hz. Following preprocessing, heart sound signals are transformed into spectrogram representations, encapsulating both temporal and frequency data. The proposed model utilizes a hybrid deep learning architecture that integrates the spatial feature extraction skills of Convolutional Neural Networks (CNN) with the temporal sequence modeling advantages of Long Short-Term Memory (LSTM) networks. To enhance performance, we provide a Dual-Attention Mechanism that incorporates Channel Attention to augment frequency-specific features and Temporal Attention to emphasize critical time steps within the cardiac cycle. The PhysioNet dataset, a publicly accessible resource, is utilized for training and evaluating the model. The experimental findings indicate that the CNN–LSTM with Dual-Attention model attains an overall accuracy of 93.29%. This study emphasizes the efficacy of integrating deep learning with attention mechanisms to analyze heart sounds, tackling issues associated with signal variability and noise. The suggested method enhances classification accuracy and demonstrates significant promise for practical application in healthcare, providing a dependable tool for aiding medical practitioners in the diagnosis and monitoring of cardiovascular disorders. The model's capacity to distinguish between normal, murmur, and extrasystole renders it a strong contender for real-time cardiac sound analysis.

@article{Jamal2026,
  title     = {Improving Heart Sound Diagnosis with a Combined CNN-LSTM and Dual-Attention Deep Learning Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Arshad Jamal and R. Kanesaraj Ramasamy and Junaidi Abdullah},
  doi       = {10.14569/IJACSA.2026.0170405},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170405}
}