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Updated: May 12, 2026

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Detecting multiple fiducial markers from a camera seismocardiogram.

Haozhe Li1, Dongmin Huang2, Lin Liu1

  • 1Shandong University of Science and Technology, Qingdao, 266590, China.

Biomedical Optics Express
|May 11, 2026
PubMed
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This study introduces a new camera-based system and a deep learning model (DiAT) for analyzing seismocardiogram (SCG) signals. The method accurately detects key cardiac events, aiding in early cardiovascular event detection.

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Monitoring
  • Signal Processing

Background:

  • Seismocardiogram (SCG) signals offer insights into cardiac contractions and valve function for early cardiovascular event detection.
  • Accurate identification of SCG fiducial markers is essential for estimating hemodynamic parameters.

Purpose of the Study:

  • To develop a novel framework for measuring SCG signals using a defocused camera-based speckle imaging system.
  • To create a deep learning model (DiAT) for detecting multiple fiducial markers in SCG signals.

Main Methods:

  • A defocused camera-based speckle imaging system was used to acquire SCG signals.
  • A deep learning model, DiAT, employing dilated convolution and multi-head attention, was developed to detect seven fiducial markers (MC, IM, AO, IC, RE, AC, MO).

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  • Evaluation metrics included percentage of predictions within ±1 ms of true values and Mean Absolute Error (MAE).

    • Main Results:

    • The DiAT model achieved 87.78% accuracy in detecting seven fiducial markers.
    • The Mean Absolute Error (MAE) was 8.4106 ms.
    • Experiments were conducted on 17 adult subjects using an ice water stimulation protocol.

    Conclusions:

    • The proposed camera-SCG measurement and DiAT deep learning model demonstrate reliable performance.
    • This framework shows potential for accurate fiducial marker detection in SCG signals.
    • The method supports enhanced monitoring for adverse cardiovascular events.