S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2805

Heart Sound Classification Based on Nonlinear Time-frequency Features

Aaron Raymond See, Inah Salvador H. Cabili, and Yeou-Jiunn Chen

(Received May 25, 2021; Accepted December 2, 2021)

Keywords: heart sound classification, Shannon entropy, spectral entropy, support vector machine

Cardiovascular disease (CVD) has been the most common factor of death for decades, and one method to detect CVD is through heart sound auscultation. Numerous studies have investigated improvements in precision and accuracy for heart sound classification using machine learning. Nonetheless, most methods utilize many features in their machine learning to increase the accuracy of their predictive model to address challenges associated with signals acquired through sensors placed at different locations. In this paper, we propose the use of heart sounds segmented into three frequency bands and the extraction of features, namely the Shannon entropy and spectral entropy of each frequency band, to serve as an input to our support vector machine (SVM). The focus of the study is to examine the use of only six features to achieve a satisfactory score in heart sound classification. The technique is assessed using an online heart sound database. The features that were extracted are trained and tested using the SVM to predict normal and abnormal heart sounds. Results demonstrated accuracies of 95% and 78% for normal and abnormal heart sounds, respectively. Subsequently, the testing results achieved an overall accuracy of 82.5% with 85% sensitivity and 80% specificity.

Corresponding author: Yeou-Jiunn Chen




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