A novel heart-mobile interface for detection and classification of heart sounds. (August 2018)

• Record Type: Journal Article
• Authors: Thiyagaraja, Shanti R.; Dantu, Ram; Shrestha, Pradhumna L.; Chitnis, Anurag; Thompson, Mark A.; Anumandla, Pruthvi T.; Sarma, Tom; Dantu, Siva
• Is Part Of: Biomedical signal processing and control. Volume 45(2018)
• Page Start: 313
• Page End: 324
• Publication Date: 2018-08
• Subjects: Split detection -- Heart sound classification -- Smartphone application -- Signal processing -- Mel-Frequency Cepstral Coefficient -- Hidden Markov Model; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.05.008
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Novel method for detection and classification of heart sounds is proposed. Uses low-cost customized stethoscope (30$) along with a smartphone application. Classification model based on MFCC and HMM. Detects 16 types of heart sounds. Across all abnormal murmurs, on average 92.68% were correctly classified. Abstract: 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.