An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds
Article
Article Title | An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Barua, Prabal Datta, Karasu, Mehdi, Kobat, Mehmet Ali, Balik, Yunus, Kivrak, Tarik, Baygin, Mehmet, Dogan, Sengul, Demir, Fahrettin Burak, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 146, pp. 1-10 |
Article Number | 105599 |
Number of Pages | 10 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105599 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482522003912 |
Abstract | Background and purpose: Valvular heart disease (VHD) is an important cause of morbidity and mortality. Echocardiography is the reference standard for VHD diagnosis but is not universally accessible. Manual cardiac auscultation is inadequate for screening VHD. Many machine learning models using heart sounds acquired with an electronic stethoscope may improve the accuracy of VHD diagnosis. We aimed to develop an accurate sound classification model for VHD diagnosis. Materials and methods: A new large stethoscope sound dataset containing 10,366 heart sounds divided into ten categories (nine VHD and one healthy) were prospectively collected. We developed a handcrafted learning model that comprised multilevel feature extraction based on a dual symmetric tree pattern (DSTP) and multilevel discrete wavelet transform (DWT), feature selection, and classification. The multilevel DWT was used to create sub-bands to extract features at both high and low levels. Then, iterative neighborhood component analysis was used to select the most discriminative 512 features from among the extracted features in the generated feature vector. In the classification phase, a support vector machine (SVM) was used with 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV. Results: Our proposed DSTP-based model attained 99.58% and 99.84% classification accuracies using SVM classifier with 10-fold CV and LOSO CV, respectively. Conclusions: The presented DSTP-based classification model attained excellent multiclass classification performance on a large prospective heart sound dataset at a low computational cost. |
Keywords | Cardiologic disorders detection; Dual symmetric tree pattern; Machine learning; Stethoscope sound classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
School of Business | |
University of Technology Sydney | |
Divan Hospital, Turkiye | |
Firat University, Turkey | |
Ardahan University, Turkiye | |
Bandirma Onyedi Eylul University, Turkiye | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore |
https://research.usq.edu.au/item/yyw87/an-accurate-valvular-heart-disorders-detection-model-based-on-a-new-dual-symmetric-tree-pattern-using-stethoscope-sounds
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