Method for Extracting Arterial Pulse Waveforms from Interferometric Signals

Abstract

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry-Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors' knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min-max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology's performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry-Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice.

Keywords: Fabry–Perot interferometer; Python-based processing; arterial pulse waveform.

Figure 1

Schematic of our low-finesse Fabry–Perot cavity formed between the fiber end and a thin elastic membrane.

Figure 2

A typical interference signal $I\left(t\right)$ for our low-finesse FPI. Green symbols indicate extrema, and red symbols denote extrema associated with transitions in the cardiac cycle.

Figure 3

Schematic diagram briefly summarizing the entire data processing.

Figure 4

Smoothed envelope with filtered peaks by distance and height. The figure shows the smoothed envelope of the signal (blue line) with detected peaks (red dots) that meet specific distance and height criteria.

Figure 5

Averaged signal with detected minima and confidence interval. The figure illustrates the averaged signal (solid blue line) with a 95% confidence interval (shaded blue area) and highlights the detected minima (red points). Several such local extrema can be identified in the signal. These can be related to possible breakpoints.

Figure 6

A segment of the time evolution of the interference signal, highlighting critical points for analysis. Red and green symbols denote interference extrema. Dashed red lines represent the statistically estimated positions of breakpoints, and the found breakpoints are marked by red symbols.

Figure 7

Time evolution of the displacement for one pulse (red curve), with the blue curve representing the measured interference intensity.

Figure 8

Reconstructed FPC length change ($\mathrm{\Delta }z$) with simultaneous measurement of ECG signal over time (approx. 5 s) for three subjects. The periodic waveform reflects arterial pulsations. The measurements were taken at the radial pulse point.