@article {300,
	title = {A novel heart-mobile interface for detection and classification of heart sounds},
	journal = {Biomedical Signal Processing and Control},
	volume = {45},
	year = {2018},
	pages = {313 - 324},
	abstract = {<p>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{\textquoteright}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.</p>
},
	keywords = {Hidden Markov Model},
	issn = {1746-8094},
	doi = {https://doi.org/10.1016/j.bspc.2018.05.008},
	url = {https://www.sciencedirect.com/science/article/pii/S1746809418301101},
	author = {Thiyagaraja, Shanti and Ram Dantu and Shrestha, Pradhumna and Chitnis, Anurag and Thompson, Mark and Anumandla, Pruthvi and Sarma, Tom and Dantu, Siva}
}
@conference {216,
	title = {Smart phone monitoring of second heart sound split},
	booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2014},
	month = {Aug},
	abstract = {<p>Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.</p>
},
	keywords = {acoustic signal processing, aortic component, biomedical equipment, Biomedical monitoring, cardiac diagnosis, cardiology, continuous wavelet transform, Continuous wavelet transforms, discrete wavelet transform, discrete wavelet transforms, fast Fourier transform, fast Fourier transforms, frequency domain, Heart, heart auscultation, heartbeat recording, medical signal processing, Monitoring, patient diagnosis, patient monitoring, pulmonic component, second heart sound split, smart phone monitoring, smart phones, stethoscope},
	doi = {10.1109/EMBC.2014.6944050},
	author = {Thiyagaraja, Shanti and Vempati, Jagannadh and Ram Dantu and Sarma, Tom and Dantu, Siva}
}
