Classification of Normal and Abnormal Heart Sounds Using Hjorth Features and LSTM – RNN Algorithm

• Published in: Journal of physics. Conference series Vol. 2673; no. 1; pp. 12003 - 12016
• Main Authors: Rizal, A, Fauzi, H, Wulandari, D R, Zamhari, D K
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2023
• Subjects: Algorithms; Classification; Feature extraction; Physics
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2673/1/012003

The stethoscope is a commonly used medical device for diagnosing a patient’s condition by listening to their heartbeat. This diagnostic technique is known as auscultation, where each heart sound heard through the stethoscope has a distinct pattern that depends on the person’s heart condition. Researchers have developed computational methods that automatically analyze heart sounds to overcome the subjective nature of auscultation. In this study, we utilized the Hjorth method for feature extraction and the Long Short-Term RNN algorithm for classifying the normal and abnormal heart sounds. With 100 epochs, two layers of LSTM-RNN, and one layer of Dense, the classification accuracy for distinguishing normal and abnormal sounds reached 71.95%. This research aims to contribute to the development of an accurate system for detecting normal and abnormal heart sounds.