A Novel Machine Learning-Based Heart Murmur Detection and Classification using Sound Feature Analysis


Ram Sivaraman1 and Joe Xiao2, 1Liberal Arts and Science Academy, USA, 2Optum/United Health Care, USA


An electrocardiogram (ECG) is a common method used for diagnosis of heart diseases. ECG is not sufficient to detect heart abnormalities early. Heart sound monitoring or phonocardiogram (PCG) is a non-invasive assessment that can be performed during routine exams. PCG can provide valuable details for both heart disorder diagnosis as well as any perioperative cardiac monitoring. Further, heart murmurs are abnormal signals generated by turbulent blood flow in the heart and are closely associated with specific heart diseases. This paper presents a new machine learning-based heart sounds evaluation for murmurs with high accuracy. A random forest classifier is built using the statistical moments of the coefficients extracted from the heart sounds. The classifier can predict the location of the heart sounds with over 90% accuracy. The random forest classifier has a murmur detection accuracy of over 70% for test dataset and detects with over 98% accuracy for the full dataset.


Random Forest Network, Phonocardiogram, Heart Murmur, Sound Featureslity control

Full Text  Volume 14, Number 2