Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Selvaraj, Nandakumar; Jaryal, Ashok Kumar; Santhosh, Jayasree; Deepak, K K; Anand, Sneh

doi:10.1080/03091900701781317

Cited by 286 publications

(199 citation statements)

References 12 publications

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“…Previous studies comparing PRV derived from PPG and ECG under optimal rest conditions did not report significant differences in PRV assessment. 6,45,46 However, studies that compared PPG and ECG under less optimal conditions have shown that PRV PPG assessed the parameters of short-term variability (HF, RMSSD, and SD1) considerable less reliable compared to ECG. 7,9 In line with these findings, our results showed that the cold temperature change (COLD) exhibited significant increases of the parameters reflecting short-term variability (i.e., HF activity, RMSSD, and SD1), whereas the parameters reflecting long-term variability did not change significantly compared to the control condition (CONTROL).…”

Section: Sympathetic and Parasympathetic Autonomous Nervous System Comentioning

confidence: 99%

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

2016

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Pulse rate variability (PRV) can be extracted from functional near-infrared spectroscopy (fNIRS) (PRV(NIRS)) and photoplethysmography (PPG) (PRV(PPG)) signals. The present study compared the accuracy of simultaneously acquired PRV(NIRS) and PRV(PPG), and evaluated their different characterizations of the sympathetic (SNS) and parasympathetic (PSNS) autonomous nervous system activity. Ten healthy subjects were recorded during resting-state (RS) and respiratory challenges in two temperature conditions, i.e., room temperature (23°C) and cold temperature (4°C). PRV(NIRS) was recorded based on fNIRS measurement on the head, whereas PRV(PPG) was determined based on PPG measured at the finger. Accuracy between PRV(NIRS) and PRV(PPG), as assessed by cross-covariance and cross-sample entropy, demonstrated a high degree of correlation (r > 0.9), which was significantly reduced by respiration and cold temperature. Characterization of SNS and PSNS using frequency-domain, time-domain, and nonlinear methods showed that PRV(NIRS) provided significantly better information on increasing PSNS activity in response to respiration and cold temperature than PRV(PPG). The findings show that PRV(NIRS) may outperform PRV(PPG) under conditions in which respiration and temperature changes are present, and may, therefore, be advantageous in research and clinical settings, especially if characterization of the autonomous nervous system is desired. Abstract. Pulse rate variability (PRV) can be extracted from functional near-infrared spectroscopy (fNIRS) (PRV NIRS ) and photoplethysmography (PPG) (PRV PPG ) signals. The present study compared the accuracy of simultaneously acquired PRV NIRS and PRV PPG , and evaluated their different characterizations of the sympathetic (SNS) and parasympathetic (PSNS) autonomous nervous system activity. Ten healthy subjects were recorded during resting-state (RS) and respiratory challenges in two temperature conditions, i.e., room temperature (23°C) and cold temperature (4°C). PRV NIRS was recorded based on fNIRS measurement on the head, whereas PRV PPG was determined based on PPG measured at the finger. Accuracy between PRV NIRS and PRV PPG , as assessed by cross-covariance and cross-sample entropy, demonstrated a high degree of correlation (r > 0.9), which was significantly reduced by respiration and cold temperature. Characterization of SNS and PSNS using frequency-domain, time-domain, and nonlinear methods showed that PRV NIRS provided significantly better information on increasing PSNS activity in response to respiration and cold temperature than PRV PPG . The findings show that PRV NIRS may outperform PRV PPG under conditions in which respiration and temperature changes are present, and may, therefore, be advantageous in research and clinical settings, especially if characterization of the autonomous nervous system is desired.

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Section: Sympathetic and Parasympathetic Autonomous Nervous System Comentioning

confidence: 99%

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

2016

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“…PRV analysis has been performed in multiple studies using PPG signals recorded from the finger or earlobe and report varying levels of accuracy compared to HRV (2,4,5,7,8,15,21). By comparison to ECG signals, PPG waveforms are characteristically smooth and do not contain a clearly defined and detectable 'landmark' feature.…”

Section: Introductionmentioning

confidence: 99%

Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals

Hemon

Phillips

2015

J Clin Monit Comput

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link ABSTRACT PurposeThe suitability of different methods of finding the foot point of a pulse as measured using earlobe photoplethysmography during stationary conditions was investigated. MethodsInstantaneous pulse period (PP) values from PPG signals recorded from the ear in healthy volunteer subjects were compared with simultaneous ECG-derived cardiac periods (RR interval). Six methods of deriving pulse period were used, each based on a different method of finding specific landmark points on the PPG waveform. These methods included maximum and minimum value, maximum first and second derivative, 'intersecting tangents' and 'diastole patching' methods. Selected time domain HRV variables were also calculated from the PPG signals obtained using multiple methods and compared with ECG-derived HRV variables. ResultsThe correlation between PPG and ECG was greatest for the intersecting tangents method compared to the other methods (RMSE = 5.69 ms, r 2 = 0.997). No significant differences between PP and RR were seen for all PPG methods, however the PRV variables derived using all methods showed significant differences to HRV, attributable to the sensitivity of PRV parameters to pulse transients and artifacts. ConclusionsThe results suggest that the intersecting tangents method shows the most promise for extracting accurate pulse rate variability data from PPG datasets. This work has applications in other areas where pulse arrival time is a key measurement including pulse wave velocity assessment. KEYWORDSPulse rate variability, photoplethysmography, foot-finding, pulse wave velocity.

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“…The temporal characteristics of PPG are studied using the peak-to-peak intervals among pulses. These intervals have been shown to correspond directly to the inter-peak intervals of ECG [22], and hence can be characterized using well-known ECG inter-beat features [14]. These include temporal features such as mean and standard deviation of heart rate and frequency domain features such as LF/HF ratio.…”

Section: Background and Related Workmentioning

confidence: 99%

Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal

Nabar

Banerjee

Gupta

et al. 2011

Proceedings of the 2nd Conference on Wireless Health

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Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: MIMIC database and data collected using commercial wearable sensors. Results show significant bandwidth and energy savings due to data transmission reduction. The average reduction in data size for wearable sensor-based data is 300:1, while maintaining a diagnostic accuracy above 94%.

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Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Cited by 286 publications

References 12 publications

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals

Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal

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