TY - JOUR
T1 - A novel heart-mobile interface for detection and classification of heart sounds
AU - Thiyagaraja, Shanti R.
AU - Dantu, Ram
AU - Shrestha, Pradhumna L.
AU - Chitnis, Anurag
AU - Thompson, Mark A.
AU - Anumandla, Pruthvi T.
AU - Sarma, Tom
AU - Dantu, Siva
N1 - Funding Information:
The Institutional Review Board at UT Southwestern Medical Center has approved this study. The Institutional Review Board waived the need for informed consent. This work is also approved by the University of North Texas Institutional Review Board (IRB Number13-086). The authors would like to thank the National Science Foundation (Grant Numbers (IIS 1545599 and CNS 1637291)) for partially funding this research.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - Heart sound classification
KW - Hidden Markov Model
KW - Mel-Frequency Cepstral Coefficient
KW - Signal processing
KW - Smartphone application
KW - Split detection
UR - http://www.scopus.com/inward/record.url?scp=85048823521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048823521&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2018.05.008
DO - 10.1016/j.bspc.2018.05.008
M3 - Article
AN - SCOPUS:85048823521
SN - 1746-8094
VL - 45
SP - 313
EP - 324
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
ER -