“Lub and Dub”: An Optimized Approach for Heart Disease Classification Based on Heart Sound Using Bi-LSTM
Keywords:
Bi-LSTM, Hear sound, classification, deep learning, spectrogram features.Abstract
CVD detection strategies encompass statistical, image-based, and audio-based approaches, emphasizing analyzing systolic and diastolic sounds. While statistical methods rely on traditional risk factors, image-based techniques utilize deep learning, particularly CNNs, for early detection by analyzing Electrocardiogram data. Audio-based methods, including time-frequency analysis of phonocardiogram signals, show promise in detecting cardiovascular abnormalities, yet specific sound disorders remain insufficiently addressed. Real-time monitoring of systolic and diastolic sound irregularities holds potential for mitigating heart attack risks. Recent observations underscore the critical need for dynamic, real-time monitoring, shifting from conventional systematic assessments to ongoing observations. This paper introduces a Bi-LSTM model to detect abnormal heart sound patterns, achieving an accuracy of 0.74 and demonstrating a favorable ROC curve across all classes.
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