Educational Background

Current Courses

BME 4370 Biomedical Control Systems

BME 3240: Computational Methods for Biological Systems

BME 3222 Bio Signals and Systems

BME 4252 Biomedical Measurements and Instrumentation

BME 4253 Biomedical Imaging and Instrumentation Laboratory

Previous courses..

BME 5720 Biomedical Instrumentation

BME 5702 Biomedical Applications in Physiology 

BME 4050 Cardiovascular Engineering

BME 5790 Numerical Modeling in Biomedical Engineering

Selected Publications

Mann, A., Gamage, P., Kakavand, B., & Taebi, A. (2024). Exploring the Impact of Sensor Location on Seismocardiography-Derived Cardiac Time Intervals. Journal of Engineering and Science in Medical Diagnostics and Therapy, 1-19.

Davidson, H., Scardino, B., Gamage, P. T., & Taebi, A. (2024). Variations of Middle Cerebral Artery Hemodynamics Due to Aneurysm Clipping Surgery. Journal of Engineering and Science in Medical Diagnostics and Therapy, 7(1), 011008. 

Azad, M. K., Gamage, P. T., Dhar, R., Sandler, R. H., & Mansy, H. A. (2023). Postural and longitudinal variability in seismocardiographic signals. Physiological Measurement, 44(2), 025001. 

Sandler, R. H., Hassan, T., Rahman, B., Gamage, P., & Mansy, H. (2023). Effect Of Extra Soft Tissue On Seismocardiographic Signal Using Finite Element Analysis. Journal of Cardiac Failure, 29(4), 599.

 Ye, G., Gamage, P. T., Balasubramanian, V., Li, J. K. J., Subasi, E., Subasi, M. M., & Kaya, M. (2023). Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models. Applied Sciences, 13(8), 5191.

Gharaibeh, Y., Lee, J., Zimin, V. N., Kolluru, C., Dallan, L. A., Pereira, G. T., Kim, J. N., Hoori, A., Dong, P., Gamage, P. T., Gu, L., Bezerra, H. G., & Wilson, D. L. (2023). Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images. Scientific Reports, 13(1), 1-12

Azad, M. K., Gamage, P. T., Dhar, R., Sandler, R. H., & Mansy, H. A. (2023). Postural and longitudinal variability in seismocardiographic signals. Physiological Measurement.

Gamage, P. T., Dong, P., Lee, J., Gharaibeh, Y., Zimin, V. N., Bezerra, H. G., ... & Gu, L. (2022). Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches. Applied Sciences, 12(11), 5573.

Shokrollahi, Y., Dong, P., Gamage, P. T., Patrawalla, N., Kishore, V., Mozafari, H., & Gu, L. (2022). Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels. Applied Sciences, 12(21), 10835.

Lin, S., Premaraj, T. S., Gamage, P. T., Dong, P., Premaraj, S., & Gu, L. (2022). Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index. Life, 12(7), 1080.

Azad M.K., D’Angelo J., Gamage P.T., Ismail S., Sandler R.H., Mansy H.A. (2021) Spatial Distribution ofSeismocardiographic Signals. In: Obeid I., Selesnick I., Picone J. (eds) Biomedical Signal Processing. Springer, Cham.

Gamage, P. T., Dong, P., Lee, J., Gharaibeh, Y., Zimin, V. N., Dallan, L. A., ... & Gu, L. (2021). Hemodynamic alternations following stent deployment and post-dilation in a heavily calcified coronary artery: In silico and exvivo approaches. Computers in Biology and Medicine, 139, 104962.

Khalili, F., Gamage, P. T., Taebi, A., Johnson, M. E., Roberts, R. B., & Mitchell, J. (2021). Spectral Decomposition of the Flow and Characterization of the Sound Signals through Stenoses with Different Levels of Severity. Bioengineering, 8(3), 41.

Khalili, F., Gamage, P. T., Taebi, A., Johnson, M. E., Roberts, R. B., & Mitchel, J. (2021). Spectral Decomposition and Sound Source Localization of Highly Disturbed Flow through a Severe Arterial Stenosis. Bioengineering, 8(3), 34.

Gamage P.T., Azad M.K., Taebi A., Sandler R.H., Mansy H.A. (2020) Clustering of SCG Events Using Unsupervised Machine Learning. In: Obeid I., Selesnick I., Picone J. (eds) Signal Processing in Medicine and Biology. Springer, Cham.

Sandler, R. H., Gamage, P., Azad, M. K., Dhar, R., Spiewak, A., Raval, N. Y., ... & Mansy, H. (2020). Computational and Mechanical Modeling of SCG Signals. Journal of Cardiac Failure, 26(10), S88.

 Sandler, R., Gamage, P., Azad, M. K., Dhar, R., Raval, N., Mentz, R., & Mansy, H. (2020). Potential SCG Predictors of Heart Failure Readmission. Journal of Cardiac Failure, 26(10), S87.

 Sandler, R. H., Azad, M. K., D'Angelo, J., Gamage, P., Raval, N. Y., Mentz, R. J., & Mansy, H. A. (2020). Documenting Spatial Variation of SCG Signals for Optimal Sensor Placement. Journal of Cardiac Failure, 26(10), S92.

Sandler, R. H., Azad, K., Rahman, B., Taebi, A., Gamage, P., Raval, N., ... & Mansy, H. A. (2019). Minimizing Seismocardiography Variability by Accounting for Respiratory Effects. Journal of Cardiac Failure, 25(8), S185.

 Khalili, F., Gamage, P., Sandler, R., & Mansy, H. (2018). Adverse hemodynamic conditions associated with mechanical heart valve leaflet immobility. Bioengineering, 5(3), 74.

 Azad, M. K., Gamage, P. T., & Sandler, R. H. (2018). Detection of respiratory phase and rate from chest surface measurements. J Appl Biotechnol Bioeng, 5(6), 359-362.

 Gamage, P. P.T., Khalili, F., Khurshidul Azad, M. D., and Mansy, H. A. (March 19, 2018). "Modeling Inspiratory Flow in a Porcine Lung Airway." ASME. J Biomech Eng. June 2018; 140(6): 061003

Research

Dr. Gamage's research primarily focuses on advancing non-invasive, cost-effective diagnostic and monitoring strategies for diseases. This involves a multidisciplinary approach, integrating computational modeling, instrumentation, signal processing, and artificial intelligence (AI) techniques.

Computational Modeling: Complex physiological processes within the body are simulated using computational models, providing insights into disease development and progression, aiding in outcome prediction, and the design of targeted interventions. Diagnostic and treatment strategies are refined before clinical application by exploring various scenarios within a controlled virtual environment.

Instrumentation: Patient data is collected non-invasively using advanced medical instrumentation, including cutting-edge imaging devices, wearable sensors, and sophisticated tools for monitoring vital physiological parameters. This allows for comprehensive data collection while minimizing patient discomfort and healthcare costs associated with invasive procedures.

Signal Processing: Using sophisticated signal processing techniques, meaningful information is extracted from medical instrument data. Algorithms analyze physiological signals to identify subtle patterns indicative of disease states, enabling early detection, accurate diagnosis, and continuous disease monitoring, empowering healthcare professionals to tailor treatment plans to individual patient needs.

Artificial Intelligence (AI): Raw data is transformed into actionable insights using AI algorithms. Machine learning models, trained on acquired datasets, automate complex analyses, uncover hidden patterns within patient data, and develop predictive models for disease progression and treatment response. Personalized healthcare interventions are facilitated, optimizing patient outcomes while minimizing resource utilization and healthcare costs.

In summary, integrating computational modeling, instrumentation, signal processing, and AI techniques represents a cutting-edge approach to healthcare research, facilitating innovative diagnostic and monitoring strategies tailored to individual patient needs.