A Nonlinear Spectral Approach for Radar-Based Heartbeat Estimation via Autocorrelation of Higher Harmonics

TL;DR

This paper introduces a nonlinear spectral method that leverages autocorrelation of higher harmonics to improve radar-based heartbeat interval estimation, effectively reducing noise and respiratory interference for more accurate measurements.

Contribution

The proposed approach uniquely enhances heartbeat periodicity detection by nonlinear processing of the spectrum, outperforming conventional filtering methods in robustness and accuracy.

Findings

Reduces root-mean-square error by 20%

Improves correlation coefficient by 0.20

Demonstrates robustness against noise and respiratory interference

Abstract

This study presents a nonlinear signal processing method for accurate radar-based heartbeat interval estimation by exploiting the periodicity of higher-order harmonics inherent in heartbeat signals. Unlike conventional approaches that employ selective frequency filtering or track individual harmonics, the proposed method enhances the global periodic structure of the spectrum via nonlinear correlation processing. Specifically, smoothing and second-derivative operations are first applied to the radar displacement signal to suppress noise and accentuate higher-order heartbeat harmonics. Rather than isolating specific frequency components, we compute localized autocorrelations of the Fourier spectrum around the harmonic frequencies. The incoherent summation of these autocorrelations yields a pseudo-spectrum in which the fundamental heartbeat periodicity is distinctly emphasized. This nonlinear approach mitigates the effects of respiratory harmonics and noise, enabling robust interbeat interval estimation. Experiments with radar measurements from five participants demonstrate that the proposed method reduces root-mean-square error by 20% and improves the correlation coefficient by 0.20 relative to conventional techniques.