A deep learning approach using 1D-CNN and LSTM classified heart sounds with high accuracy, achieving 95% for normal, 94% for murmur, 95% for extra-systole, and 96% for artifact cases.
Do deep learning models (1D-CNN and LSTM) accurately classify heart sounds into normal, murmur, extra-systole, and artifact categories?
A deep learning approach combining 1D-CNN and LSTM can accurately classify heart sounds into normal, murmur, extra-systole, and artifact categories, demonstrating potential for automated noninvasive diagnostics.
The Internet of Medical Things (IoMT) has emerged as a transformative force in healthcare, enabling the development of innovative and noninvasive diagnostic tools that leverage connected technologies. This research focuses on the application of IoMT in conjunction with deep learning techniques to detect and classify heart diseases through the analysis of heart sounds, a method that promises to enhance the accuracy and accessibility of diagnostics. We proposed frameworks that utilize advanced signal processing algorithms to capture and analyze heart sound data, that is, phonocardiograms (PCGs) collected from District Headquarters Hospital, Bahawalnagar, Pakistan. Deep learning models, specifically convolutional neural networks (CNN) and long short‐term memory (LSTM), and a combination of these are employed to identify patterns associated with various cardiac conditions. This study involves a comprehensive dataset comprising annotated heart sounds from diverse demographics, ensuring the robustness and generalizability of the proposed models. The proposed methodology involves using three models: one‐dimensional CNN (1D‐CNN), LSTM, and a combination of 1D‐CNN + LSTM to classify heart sounds into normal, murmur, extra‐systole, and artifact categories. Results demonstrate high accuracy in the classification of common heart diseases, including normal, murmur, extra‐systole, and artifact, surpassing traditional diagnostic methods. The model achieved the highest accuracy for normal cases as of 95%, for murmur cases as of 94%, for extra‐systole cases as of 95%, and for artifact cases as of 96%. This approach holds significant potential for improving patient outcomes through timely and accurate diagnostics, thus exemplifying the power of digital health innovations in modern medicine.
Sajid et al. (Thu,) conducted a other in Heart diseases (heart sound classification). Deep learning models (1D-CNN, LSTM, and 1D-CNN + LSTM) vs. Traditional diagnostic methods was evaluated on Classification accuracy for normal, murmur, extra-systole, and artifact categories. A deep learning approach using 1D-CNN and LSTM classified heart sounds with high accuracy, achieving 95% for normal, 94% for murmur, 95% for extra-systole, and 96% for artifact cases.
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