Classification of heart sound signals using a novel deep WaveNet model

Shu Lih Oh; V Jahmunah; Chui Ping Ooi; Ru-San Tan; Edward J Ciaccio; Toshitaka Yamakawa; Masayuki Tanabe; Makiko Kobayashi; U Rajendra Acharya

doi:10.1016/j.cmpb.2020.105604

Classification of heart sound signals using a novel deep WaveNet model

Comput Methods Programs Biomed. 2020 Nov:196:105604. doi: 10.1016/j.cmpb.2020.105604. Epub 2020 Jun 12.

Authors

Shu Lih Oh¹, V Jahmunah¹, Chui Ping Ooi², Ru-San Tan³, Edward J Ciaccio⁴, Toshitaka Yamakawa⁵, Masayuki Tanabe⁶, Makiko Kobayashi⁵, U Rajendra Acharya⁷

Affiliations

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.

PMID: 32593061
DOI: 10.1016/j.cmpb.2020.105604

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.

Keywords: 10-fold cross validation; Aortic stenosis; Mitral regurgitation; Mitral stenosis; Mitral valve prolapse; Phonocardiograms; WaveNet model.

MeSH terms

Aortic Valve Stenosis*
Heart Sounds*
Heart Valve Diseases*
Humans
Mitral Valve Insufficiency*