期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 45, 期 -, 页码 313-324出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2018.05.008
关键词
Split detection; Heart sound classification; Smartphone application; Signal processing; Mel-Frequency Cepstral Coefficient; Hidden Markov Model
资金
- National Science Foundation [IIS 1545599, CNS 1637291]
Diagnosis of heart disease requires that a medical practitioner investigate heart auscultations for irregular sounds, followed by echocardiography and electrocardiography tests. These expensive tests also require specialized technicians to operate. We present a low-cost, patient-centered device for the initial screening of the heart sounds that can be potentially used by the users on themselves. They can later share these readings with their healthcare providers. We have created an innovative mobile-health service platform for analyzing and classifying heart sounds. The presented system enables remote patient-monitoring by integrating advanced wireless communications with a customized low-cost stethoscope. This system also permits remote management of a patient's cardiac status while maximizing patient mobility. The smartphone application facilitates recording, processing, visualizing, listening to, and classification of heart sounds. We build our classification model using the Mel-Frequency Cepstral Coefficient and Hidden Markov Model. This application is tested in a hospital environment to collect live recordings from patients with positive results. The smartphone application correctly detected 92.68% of abnormal heart conditions in clinical trials at UT Southwestern Hospital. (C) 2018 Elsevier Ltd. All rights reserved.
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