Abstract
This chapter introduces feature extraction techniques that extract relevant features of interest from compressively sampled biosignals directly from compressed data circumventing the computationally complex reconstruction process. State-of-the-art compressed domain feature extraction process for that leverages on Johnson–Lindenstrauss lemma is presented. It is also shown that such approach is inadequate in the context of feature extraction for compressively sampled photoplethysmogram (PPG) signals. Lomb-Scargle periodogram (LSP) is introduced as an alternate approach for extracting the spectral features from compressively sampled PPG signals, which is then used to estimate the average heart rate (HR) and heart rate variability (HRV). The efficacy of the proposed approach is demonstrated through simulations which indicate that the average HR estimated is accurate within ±5 beats per minute (bpm) while HRV exhibits a correlation coefficient of > 0.90 at 30 × compression ratio (CR) compared to time domain HR and HRV estimation performed on Nyquist sampled PPG signals.
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