Detecting chaotic dynamics in cardiac signals from laser Doppler vibrometry

Stephen DelMarco

doi:10.1117/12.3019074

7 June 2024 Detecting chaotic dynamics in cardiac signals from laser Doppler vibrometry

Stephen DelMarco

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330D (2024) https://doi.org/10.1117/12.3019074
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Human physiological systems are complex nonlinear systems often exhibiting chaotic dynamics. Chaotic dynamics has been found in physiological signals of diverse types, including cardiac, respiration, and gait-based signals, and has been used to identify physiological state or diagnose abnormal physical conditions. Traditionally, contact-based sensors such as electroencephalogram, electromyogram, and electrocardiogram have been used to capture physiological signal data. However the capability to acquire physiological signals at-a-distance, offers the potential to perform remote diagnostics, health monitoring, biometrics, and activity recognition. One such non-contact sensor technology, Laser Doppler Vibrometry (LDV), captures vibration signals at offset ranges from the vibration source. Prior work has shown that LDV can capture heartbeat signals through the Doppler variations imparted by micro-vibrations of the human body due to the pulsating heart. In this paper, we investigate the detection of chaotic dynamics from real LDV cardiac signals. We interpret the cardiac signal as produced by a physiological multi-dimensional dynamical system. We first reconstruct the multidimensional phase space trajectory of the signal using the delay coordinate embedding approach, according to Takens’ theorem. We identify a de-correlation time lag using mutual information to estimate the time delay, and use the FalseNearest Neighbor approach to estimate an appropriate system dimension. We use a combination of recurrence analysis, correlation dimension, and maximum Lyapunov exponent to detect chaotic dynamics. We present numerical results demonstrating the presence of chaotic dynamics in LDV cardiac signals.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Stephen DelMarco "Detecting chaotic dynamics in cardiac signals from laser Doppler vibrometry", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330D (7 June 2024); https://doi.org/10.1117/12.3019074

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