Affiliations: [a] Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China | [b] Department of Internal Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, China | [c] Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250061, Shandong, China
Correspondence:
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Corresponding authors: Shoushui Wei and Chengyu Liu, both in Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China. Tel.: +86 531 88395827; Fax: +86 531 88395827; E-mail:sswei@sdu.edu.cnandbestlcy@sdu.edu.cn
Abstract:
BACKGROUND: The usefulness of heart rate variability (HRV) in the clinical
research has been verified in numerous studies. However, it is controversy
that using pulse rate variability (PRV) as a surrogate of HRV in different
clinical applications.
OBJECTIVE: We aimed to investigate whether PRV extracted from finger pulse
photoplethysmography (Pleth) signal could substitute HRV from ECG signal
during different sleep stages by analyzing the common time-domain,
frequency-domain and non-linear indices.
METHODS: Seventy-five sleep apnea patients were enrolled. For each patient,
ECG and Pleth signals were simultaneously recorded for the whole night using
Alice Sleepware Polysomnographic System and the sleep stage signals were
automatically calculated by this System. Time-domain, frequency-domain and
non-linear indices of both HRV and PRV were calculated for each sleep stage.
RESULTS: Mann-Whitney U-test showed that for both time-domain and
frequency-domain indices, there were no statistical differences between HRV
and PRV results during all four sleep stages. For non-linear indices, sample
entropy reported statistical differences between HRV and PRV results for N1,
N2 and REM sleeps (all P< 0.01) whereas fuzzy measure entropy only reported
statistical differences for REM sleep (P< 0.05). SDNN, LF and LF/HF indices
decreased for both HRV and PRV with the sleep deepening while HF and
non-linear indices increased. In addition, there were strong and significant
correlation between HRV and PRV indices during all four sleep stages (all
P< 0.01).
CONCLUSIONS: PRV measurement could present the similar results as HRV
analysis for sleep apnea patients during different sleep stages.
