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Related Experiment Video

Updated: Jan 23, 2026

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Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation.

Yu Yao1, Guanghao Sun2, Tetsuo Kirimoto2

  • 1Translational Neuromodeling Unit, University of Zurich-ETH Zurich, Zurich, Switzerland.

Frontiers in Physiology
|June 6, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new adaptive non-linear filter to separate respiratory and cardiac signals from radar measurements. The method, locally projective adaptive signal separation (LoPASS), improves cardiac signal analysis and accuracy.

Keywords:
cardiac signalmedical radarnon-linear filteringsignal processingvital signs monitoring

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiovascular Technology

Background:

  • Electrocardiography measures electrical, not mechanical, heart activity.
  • Contactless radar offers a novel way to assess mechanical cardiac activity.
  • Respiratory movements interfere with mechanical cardiac signal measurements.

Purpose of the Study:

  • To adapt a non-linear filter for separating respiratory and cardiac signals in radar recordings.
  • To develop an adaptive algorithm for estimating filter parameters, enabling a fully automated process.
  • To evaluate the performance of the proposed method against linear filtering techniques.

Main Methods:

  • Adaptation of a non-linear filter for respiratory and cardiac signal separation from radar data.
  • Development of a novel, adaptive parameter estimation algorithm for the non-linear filter.
  • Application of the locally projective adaptive signal separation (LoPASS) method to benchmark tasks: cardiac template extraction and peak timing analysis.

Main Results:

  • LoPASS reduced the mean standard deviation of the cardiac template by at least a factor of 2.
  • 9 out of 10 subjects showed significant correlation between R-T and R-radar intervals using LoPASS, compared to 6 out of 10 with linear filters.
  • Improved preservation of cardiac signal morphology was observed with the non-linear separation method.

Conclusions:

  • The developed non-linear signal separation method with adaptive parameter estimation offers superior performance over linear filtering for radar-based cardiac activity assessment.
  • LoPASS enhances the accuracy of cardiac template extraction and peak timing analysis.
  • This method is expected to benefit beat-to-beat analysis of cardiac radar signal morphology.