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Biological Sources of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction.

As opposed to other biological signals such as electrocardiograms and heart sounds, SCG signals are not well understood yet. Thus, understanding the genesis and biological sources of these signals is among the first steps before we can use them for diagnosis purposes.

Vasculature of the Heart


Investigate the biological and pathological sources of the  cardiovascular-induced sounds and vibrations measured noninvasively on the body surface


  • Signal and image processing

  • Machine learning

  • Computational fluid dynamics and finite element modeling

Key Finding

We discovered that SCG waveforms (corresponding to cardiac cycles) change in both time and frequency domains with the lung volume, i.e. when the lung is empty versus filled with the inhaled air. Our results showed that SCG events tended to have two slightly different morphologies. This finding resulted in grouping the SCG events into clusters where the events in each cluster were more similar to each other.

Other Publications

  • Sandler, R.H., Azad, M.K., Rahman, B., Taebi, A., Gamage, P., Raval, N., Mentz, R.J., Mansy, H.A. (2019). Minimizing Seismocardiography Variability by Accounting for Respiratory Effects. Journal of Cardiac Failure 25(8): S172.
    doi 10.1016/j.cardfail.2019.07.521

  • Gamage, P.T., Azad, M.K., Taebi, A., Sandler, R.H., Mansy, H.A. (2018). Clustering Seismocardiographic Events using Unsupervised Machine Learning. IEEE Signal Processing in Medicine and Biology, Philadelphia (PA).
    doi 10.1109/SPMB.2018.8615615

  • Taebi, A., Mansy, H.A. (2017) Grouping Similar Seismocardiographic Signals Using Respiratory Information, IEEE Signal Processing in Medicine and Biology, Philadelphia (PA).
    doi 10.1109/SPMB.2017.8257053

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