Shu Lih Oh1, V Jahmunah1, Chui Ping Ooi2, Ru-San Tan3, Edward J Ciaccio4, Toshitaka Yamakawa5, Masayuki Tanabe6, Makiko Kobayashi5, U Rajendra Acharya7. 1. School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore. 2. School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore. 3. National Heart Centre, Singapore. 4. Department of Medicine - Cardiology, Columbia University, USA. 5. Department of Computer Science and Electrical Engineering, Kumamoto University, Japan. 6. Department of Computer Science and Electrical Engineering, Kumamoto University, Japan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan. 7. School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department Bioinformatics and Medical Engineering, Asia University, Taiwan. Electronic address: Rajendra_Udyavara_ACHARYA@np.edu.sg.
Abstract
BACKGROUND AND OBJECTIVES: The high mortality rate and increasing prevalence of heart valve diseases globally warrant the need for rapid and accurate diagnosis of such diseases. Phonocardiogram (PCG) signals are used in this study due to the low cost of obtaining the signals. This study classifies five types of heart sounds, namely normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation. METHODS: We have proposed a novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class. RESULTS: We have achieved a training accuracy of 97% for the classification of heart sounds into five classes. The highest classification accuracy of 98.20% was achieved for the normal class. The developed model was validated with a 10-fold cross-validation, thus affirming its robustness. CONCLUSION: The study results clearly indicate that the developed model is able to classify five types of heart sounds accurately. The developed system can be used by cardiologists to aid in the detection of heart valve diseases in patients.
BACKGROUND AND OBJECTIVES: The high mortality rate and increasing prevalence of heart valve diseases globally warrant the need for rapid and accurate diagnosis of such diseases. Phonocardiogram (PCG) signals are used in this study due to the low cost of obtaining the signals. This study classifies five types of heart sounds, namely normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation. METHODS: We have proposed a novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class. RESULTS: We have achieved a training accuracy of 97% for the classification of heart sounds into five classes. The highest classification accuracy of 98.20% was achieved for the normal class. The developed model was validated with a 10-fold cross-validation, thus affirming its robustness. CONCLUSION: The study results clearly indicate that the developed model is able to classify five types of heart sounds accurately. The developed system can be used by cardiologists to aid in the detection of heart valve diseases in patients.