Performance Analysis of the Maximum Likelihood Estimation of Signal Period Length and Its Application in Heart Rate Estimation with Reduced Respiratory Influence
Source abstract
Abstract
The remote and non-contact monitoring of human respiration and heartbeat based on radars is a safe and convenient practice. However, how to accurately estimate the heart rate is still an open issue, because the heartbeat information in radar signals is affected by respiratory harmonics. In this paper, a maximum likelihood estimation was introduced to extract the heart rate from high-pass-filtered radar heartbeat waveforms where the low-frequency respiratory and heartbeat components were attenuated. The closed-form asymptotic estimation variance of the maximum likelihood estimator was derived to describe its performance in white Gaussian noise with a high signal-to-noise ratio (SNR). The proposed method was verified using two publicly available datasets and demonstrated superior performance compared to other methods. The estimation method and the asymptotic estimation variance here described are also applicable for signal period estimation in other settings with similar conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Invasive Vital Sign Monitoring · Hemodynamic Monitoring and Therapy · Heart Rate Variability and Autonomic Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Funding
National Natural Science Foundation of China
References (28)
Show source-provided references
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