Non-invasive Low-cost Cardiovascular Activity Monitoring
We harness the power of technology to revolutionize the way we monitor heart function and diagnose cardiovascular diseases. By measuring the vibrations generated by cardiac activities such as valve opening and closure, we are able to noninvasively gather critical diagnostic information. Our expertise in computational models, signal processing, and machine learning allows us to accurately characterize these signals, leading to the development of innovative cost-effective methods for monitoring and diagnosis of cardiovascular diseases. With a focus on precision and accuracy, our work has the potential to improve patient outcomes and change the future of healthcare, particularly within low-income populations and underserved areas.
---- Electrocardiography
to measure electrical
activity of heart
---- Stethoscope
to record heart sounds
---- Accelerometer
to measure cardiovascular-
induced vibrations
---- Respiration Belt
to record respiration rate
Objective
Develop cost-effective methods for monitoring and early diagnosis of cardiovascular diseases based on cardiovascular-induced vibrations measured noninvasively on the body surface
Amirtahà Taebi
PI
Current members involved:
Past members involved:
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Skillsets
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Human subject studies
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Signal and image processing
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Machine learning
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Computational fluid dynamics and finite element modeling
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Sensor array design
Sponsors
https://doi.org/10.3390/bioengineering9040149
1) CFD-FEA simulations to predict sound and vibration propagation through body 2) in-vitro and in-vivo measurements to validate simulations 3) analysis of the signals using state-of-the-art signal processing and machine learning
https://doi.org/10.3390/bioengineering9040149
Selected Publications
et al.
Cardiovascular disease is a leading cause of death globally, highlighting the need for effective diagnostic tools for early detection and intervention. Seismocardiography (SCG) is a noninvasive method for evaluating cardiac activity, however, the complexity of SCG signals presents challenges in its application. Recent advances in low-cost sensors, signal processing, and machine learning have led to renewed interest in SCG and its potential utility. This review examines the recent progress in the field of SCG.
doi 10.3390/vibration2010005
In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions... Read more!
doi 10.3390/bioengineering9040149
Other Publications
Mann, A., Rahman, M.M., Vanga, V., Gamage, P.T., Taebi, A. (2024). Variation of Seismocardiogram-Derived Cardiac Time Intervals and Heart Rate Variability Metrics Across the Sternum. ASME J of Medical Devices 18(4): 044502.
doi 10.1115/1.4066368
Mann, A., Gamage, P.T., Kakavand, B., Taebi, A. (2024). Exploring the Impact of Sensor Location On Seismocardiography-Derived Cardiac Time Intervals. ASME J of Medical Diagnostics.
doi 10.1115/1.4063203
Ruckman, S., Bhatt, J., Cook, J., Gamage, P.T., Kakavand, B., Taebi, A. (2023). Design, Prototype, and Evaluation of a Low-Cost Multimodal Device for Cardiovascular Monitoring. ASME 2023 International Mechanical Engineering Conference and Exposition, V005T06A022.
doi 10.1115/IMECE2023-112486
Mann, A., Kakavand, B., Gamage, P.T., Taebi, A. (2023). Effect of Measurement Location on Cardiac Time Intervals Estimated by Seismocardiography. ASME 2023 International Mechanical Engineering Conference and Exposition, V005T06A070.
doi 10.1115/IMECE2023-112702
Mann, A., Cook, J., Umar, M., Khalili, F., Taebi, A. (2022). Heart Rate Monitoring Using Heart Acoustics. ASME 2022 International Mechanical Engineering Conference and Exposition, V004T05A069.
doi 10.1115/IMECE2022-96824
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 generated through stenoses of different levels of severity. Bioengineering 8(3): 41.
doi 10.3390/bioengineering8030041
Khalili, F., Gamage, P.T., Taebi, A., Johnson, M.E., Roberts, R.B., Mitchell, J. (2021). Spectral decomposition and sound source localization of highly disturbed flow through a severe arterial stenosis, Bioengineering 8(3): 34.
doi 10.3390/bioengineering8030034
Taebi, A., Sandler, R.H., Kakavand, B., Mansy, H.A. (2019). Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing. Bioengineering 6(3): 64.
doi 10.3390/bioengineering6030064
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