@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 = {

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.

}, 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 {262, title = {Optimized and Secured Transmission and Retrieval of Vital Signs from Remote Devices}, booktitle = {2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}, year = {2017}, month = {July}, abstract = {

Smartphones and other mobile platforms provide a low cost and easily accessible method of monitoring patient health, and aid healthcare professionals in early detection of disease. Immediate access to the gathered data is an essential factor in effective patient care. But the current processes used for patients{\textquoteright} vital data collection is slow and error prone. This undermines the advantages of remote monitoring that mobile platforms for health monitoring provide. In this paper, we propose to upload the patient health information to the Cloud. We investigate three different models to transfer data from the smartphone to the Cloud-perform all computations in the smartphone, perform all computations in the Cloud, and divide the computations between the smartphone and the Cloud. The second approach was found to be infeasible due to very high latency in data transfer with a delay of 2.84 seconds at an upload speed of 2500 KBytes per second. In order to protect the privacy of patients, it is required by law that the data gathered from remote monitoring by using mobile platforms must be kept private, and be secured before uploading to the Cloud. This paper explores the use of prominent public key encryption algorithms and their performance on a mobile device to securely transmit confidential electronic personal health information to the Cloud. We analyze performance of three common public key encryption schemes -RSA, Diffie-Hellman, and ECC. It is shown that 160 bit key size in ECC scheme provides the same level of security that a 1024 bit key size does in RSA and Diffie-Hellman. Further, the encryption and decryption time required by ECC is three times less than the other two schemes. Hence, ECC not only requires a smaller key size to provide the same level of security, but also faster encryption and decryption times as compared to the other two schemes. This makes ECC algorithms suitable to be implemented in resource constrained mobile platforms. We also compared ECC curves from three different standards - NIST, SECG, and Brainpool - to determine the optimum ECC curve, and key size to encrypt data in the mobile phone platform. It is shown that the Brainpool curve performed better than the other two standards when the key size is less than 521 bits. We also measured the latency of uploading encrypted data in a wide variety of WiFi and mobile networks.

}, keywords = {Brainpool curve, Cloud computing, confidential electronic personal health information, data transfer, decryption, Diffie-Hellman, Diseases, ECC algorithms, ECC curves, encryption, error prone, health care, healthcare professionals, Mobile communication, mobile device, mobile health, mobile networks, mobile phone platform, patient care, patient health information, patient health monitoring, patient vital data collection, Performance evaluation, public key cryptography, public key encryption algorithms, remote devices, remote monitoring, resource constrained mobile platforms, RSA, secured transmission, Security, servers, smart phones, smartphones, vital signs, WiFi}, doi = {10.1109/CHASE.2017.55}, author = {Thiyagaraja, Shanti and Ram Dantu and Shrestha, Pradhumna and Thompson, Mark and Smith, Christopher} } @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 = {

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.

}, 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} } @article {15, title = {Cuffless Differential Blood Pressure Estimation Using Smart Phones}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {60}, year = {2013}, pages = {1080 - 1089}, abstract = {

Smart phones today have become increasingly popular with the general public for their diverse functionalities such as navigation, social networking, and multimedia facilities. These phones are equipped with high-end processors, high-resolution cameras, and built-in sensors such as accelerometer, orientation-sensor, and light-sensor. According to comScore survey, 26.2\% of U.S. adults use smart phones in their daily lives. Motivated by this statistic and the diverse capability of smart phones, we focus on utilizing them for biomedical applications. We present a new application of the smart phone with its built-in camera and microphone replacing the traditional stethoscope and cuff-based measurement technique, to quantify vital signs such as heart rate and blood pressure. We propose two differential blood pressure estimating techniques using the heartbeat and pulse data. The first method uses two smart phones whereas the second method replaces one of the phones with a customized external microphone. We estimate the systolic and diastolic pressure in the two techniques by computing the pulse pressure and the stroke volume from the data recorded. By comparing the estimated blood pressure values with those measured using a commercial blood pressure meter, we obtained encouraging results of 95{\textendash}100\% accuracy.

}, keywords = {Blood pressure, cuffless, Estimation, finger pulse, Heart, Microphones, mobile camera, Mobile communication, mobile phone, Synchronization, Valves, vascular transit time}, doi = {10.1109/TBME.2012.2211078}, author = {Chandrasekaran, Vikram and Ram Dantu and Jonnada, Srikanth and Thiyagaraja, Shanti and Subbu, Kalyan} } @conference {95, title = {Finger Blood Flow Monitoring Using Smart Phones}, booktitle = {Proceedings of the 8th International Conference on Body Area Networks}, year = {2013}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, organization = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, address = {ICST, Brussels, Belgium, Belgium}, abstract = {

The use of smart phones in healthcare applications is growing steadily. The inbuilt sensors are used to estimate the value of physiological data from human body. With progressive innovation, smart phone based medical applications will continue to be developed at an exponential rate. In this paper, we show that a smart phone can be used to monitor the blood flow in finger, based on the pulse height obtained from the fingertips. This is achieved by using the camera lens and the flash light of the smart phone. The height of the pulse rises along with the surrounding temperature indicating that the blood flow increases when the temperature becomes warmer. This study shows that there is a potential to monitor regulation of body temperature using smart phone.

}, keywords = {finger blood flow, finger pulse, mobile health, smart phone}, isbn = {978-1-936968-89-3}, doi = {10.4108/icst.bodynets.2013.253704}, url = {http://dx.doi.org/10.4108/icst.bodynets.2013.253704}, author = {Thiyagaraja, Shanti and Ram Dantu} }