Literature DB >> 28169836

Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability?

A Choi1, H Shin.   

Abstract

Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency (f s) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland-Altman analysis. As a result, significant differences (P  <  0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at f s  ⩽  20 Hz, f s  ⩽  15 Hz, f s  ⩽10 Hz; f s  =  5 Hz, respectively. In other words, a significant difference was not seen for any variable if the f s was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that f s  ⩾25 Hz sampling frequency is used.

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Year:  2017        PMID: 28169836     DOI: 10.1088/1361-6579/aa5efa

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  24 in total

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Authors:  Hyun Jae Baek; JaeWook Shin; Gunwoo Jin; Jaegeol Cho
Journal:  J Med Syst       Date:  2017-10-24       Impact factor: 4.460

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Journal:  Circ Arrhythm Electrophysiol       Date:  2021-02-12

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Authors:  Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yufeng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg
Journal:  Cardiovasc Digit Health J       Date:  2021-01-29

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8.  Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients.

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Journal:  NPJ Digit Med       Date:  2021-05-14

9.  Intranasal oxytocin increases heart-rate variability in men at clinical high risk for psychosis: a proof-of-concept study.

Authors:  Paolo Fusar-Poli; Yannis Paloyelis; Daniel Martins; Cathy Davies; Andrea De Micheli; Dominic Oliver; Alicja Krawczun-Rygmaczewska
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10.  Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach.

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Journal:  Sensors (Basel)       Date:  2020-03-30       Impact factor: 3.576

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