Research

Development of wearable sensors to monitor cardiorespiratory fitness, functional capacity, and hemodynamics in patients with cardiovascular diseases (e.g., heart failure) using non-invasive cardiogenic signals (seismocardiogram, ballistocardiogram, electrocardiogram, photoplethysmogram) and validation against gold standard clinical procedures (e.g., cardiopulmonary exercise testing, right heart catheterization,).

Example Papers:

1. Shandhi, M.M.H., Semiz, B., Hersek, S., Goller, N., Ayazi, F. and Inan, O., 2019. Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation.IEEE journal of biomedical and health informatics.[DOI]
2. Shandhi, M.M.H., Bartlett, W.H., Heller, J.A., Etemadi, M., Young, A., Ploetz, T. and Inan, O., Estimation of Instantaneous Oxygen Uptake during Exercise and Daily Activities using a Wearable Cardio-Electromechanical and Environmental Sensor. IEEE Journal of Biomedical Health Informatics, 2020. [DOI]
3. Shandhi, M.M.H., Hersek, S., Fan, J., Sander, E., Marco, T.D., Heller, J.A., Etemadi, M., Klein, L. and Inan, O., Wearable Patch Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients with Heart Failure. Journal of Cardiac Failure, 2020. [DOI] 

Development of biomedical signal processing and AI/ML algorithms using data from custom-built and commercial wearable sensors to predict/estimate various clinical health outcomes.

Example Papers:

1. Shandhi, M.M.H.*, Cho, P.J.*, Roghanizad, A.R.*, Singh, K., Wang, W.K., Enache, O., Stern, A., Sbahi, R., Tatar, B., Fiscus, S., Khoo, Q.X., Kuo, Y., Lu, X., Hsieh, J., Kalodzitsa, A., Bahmani, A., Alavi, A., Ray, U., Snyder, M.P., Ginsburg, G.S., Pasquale., D.K., Woods, C.W., Shaw, R. and Dunn, J. A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19. npj Digital Medicine, 5, 130 (2022). [DOI] [Impact Factor: 15.357]
2. Shandhi, M.M.H., Fan, J., Heller, J.A., Etemadi, M., Klein, L. and Inan, O., 2022. Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients with Heart Failure: A Feasibility Study. Transaction of Biomedical Engineering. [DOI]
3. Shandhi, M.M.H., Bartlett, W.H., Heller, J.A., Etemadi, M., Young, A., Ploetz, T. and Inan, O., Estimation of Instantaneous Oxygen Uptake during Exercise and Daily Activities using a Wearable Cardio-Electromechanical and Environmental Sensor. IEEE Journal of Biomedical Health Informatics, 2020. [DOI]

Example Papers:

1. Shandhi, M.M.H., Singh, K., Janson, N., Ashar, P., Singh, G., Lu, B., Hillygus, S., Maddocks, J.M. and Dunn, J., 2024. Assessment of ownership of smart devices and the acceptability of digital health data sharing. npj Digital Medicine, 7(1), p.44. [DOI] [Impact Factor: 15.37] [Top Ten Downloaded Preprint: Digital Health eJournal (3rd) and Preprints with The Lancet (6th) categories]
2. Cho, P.J.,* Olaye, I.M.,* Shandhi, M.M.H.,* Daza, E.J., Foschini, L. and Dunn, J.P., 2025. Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies. The Lancet Digital Health, 7(1), pp.e23-e34. [DOI] [Impact Factor: 23.8] *co-first authors
3. Cho, P.J., Yi, J., Ho, E., Shandhi, M.M.H., Dinh, Y., Patil, A., Martin, L., Singh, G., Bent, B., Ginsburg, G., Smuck, M., Woods, C., Shaw, R. and Dunn, J.P. 2022. Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design. JMIR mHealth and uHealth, 10(4), p.e29510. [DOI]
4. Jiang, S., Ashar, P., Shandhi, M.M.H. and Dunn, J., Demographic Reporting in Biosignal Datasets: A Comprehensive Analysis of the PhysioNet Open Access Database. Lancet Digital Health (2024). [DOI] [Impact Factor: 23.8]