Time-variant traits analysis in respiratory doppler radar’s signal

Zainuddin, Suraya and Mat Ibrahim, Masrullizam and Mohd Nasir, Haslinah and Nor Razman, Nur Fatin Shazwani and Zainal Abidin, Mohd Zhafran (2026) Time-variant traits analysis in respiratory doppler radar’s signal. International Journal of Electronics and Telecommunications, 72 (1). pp. 1-7. ISSN 2081-8491

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Abstract

Doppler radar-based respiratory monitoring offers a non-contact, physiologic assessment of breathing patterns. However, the inherent time-variant nature of respiratory signals presents challenges in accurate characterisation and classification. This study investigates the analysis of time-variant traits in respiratory Doppler radar signals using a feature extraction framework that integrates statistical features, Hilbert transform, discrete wavelet transforms (DWT), and fractal dimension analysis. The methodology begins with signal pre-processing to remove noise and enhance the signal for clarity. Statistical features, including mean, skewness, and kurtosis, are extracted to quantify signal variability. The Hilbert transform is employed to analyse instantaneous amplitude and phase variations, while DWT is used for multi-resolution decomposition to capture respiratory signal dynamics across different frequency scales over time. Additionally, fractal dimension analysis provides insights into the complexity and irregularity of breathing patterns in the time series. Machine learning-based classification models are applied to distinguish between normal and abnormal respiratory conditions. Results demonstrate the effectiveness of the proposed approach in enhancing respiratory signal characterisation and classification by utilising the Hilbert Transform over a Subspace Discriminant model with an accuracy rate of 92.3%. The findings suggest that integrating these feature extraction techniques can significantly improve non-invasive respiratory monitoring.

Item Type:	Article
Uncontrolled Keywords:	Discrete wavelet transform, Fractal dimension, Hilbert transform, Machine learning, Respiratory doppler signal, Statistical feature, Time-variant traits
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	17 Mar 2026 04:56
Last Modified:	17 Mar 2026 04:56
URI:	http://eprints.utem.edu.my/id/eprint/29604
Statistic Details:	View Download Statistic

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