Heart rate variability (HRV) in deep breathing tests and 5-min short-term recordings: agreement of ear photoplethysmography with ECG measurements, in 343 …
SW Weinschenk, RD Beise, J Lorenz - European journal of applied …, 2016 - Springer
SW Weinschenk, RD Beise, J Lorenz
European journal of applied physiology, 2016•SpringerPurpose We analyzed heart rate variability (HRV) taken by ECG and photoplethysmography
(PPG) to assess their agreement. We also analyzed the sensitivity and specificity of PPG to
identify subjects with low HRV as an example of its potential use for clinical applications.
Methods The HRV parameters: mean heart rate (HR), amplitude, and ratio of heart rate
oscillation (E–I difference, E/I ratio), RMSSD, SDNN, and Power LF, were measured during 1-
min deep breathing tests (DBT) in 343 individuals, followed by a 5-min short-term HRV (s …
(PPG) to assess their agreement. We also analyzed the sensitivity and specificity of PPG to
identify subjects with low HRV as an example of its potential use for clinical applications.
Methods The HRV parameters: mean heart rate (HR), amplitude, and ratio of heart rate
oscillation (E–I difference, E/I ratio), RMSSD, SDNN, and Power LF, were measured during 1-
min deep breathing tests (DBT) in 343 individuals, followed by a 5-min short-term HRV (s …
Purpose
We analyzed heart rate variability (HRV) taken by ECG and photoplethysmography (PPG) to assess their agreement. We also analyzed the sensitivity and specificity of PPG to identify subjects with low HRV as an example of its potential use for clinical applications.
Methods
The HRV parameters: mean heart rate (HR), amplitude, and ratio of heart rate oscillation (E–I difference, E/I ratio), RMSSD, SDNN, and Power LF, were measured during 1-min deep breathing tests (DBT) in 343 individuals, followed by a 5-min short-term HRV (s-HRV), where the HRV parameters: HR, SD1, SD2, SDNN, Stress Index, Power HF, Power LF, Power VLF, and Total Power, were determined as well. Parameters were compared through correlation analysis and agreement analysis by Bland–Altman plots.
Results
PPG derived parameters HR and SD2 in s-HRV showed better agreement than SD1, Power HF, and stress index, whereas in DBT HR, E/I ratio and SDNN were superior to Power LF and RMSSD. DBT yielded stronger agreement than s-HRV. A slight overestimation of PPG HRV over HCG HRV was found. HR, Total Power, and SD2 in the s-HRV, HR, Power LF, and SDNN in the DBT showed high sensitivity and specificity to detect individuals with poor HRV. Cutoff percentiles are given for the future development of PPG-based devices.
Conclusion
HRV measured by PPG shows good agreement with ECG HRV when appropriate parameters are used, and PPG-based devices can be employed as an easy screening tool to detect individuals with poor HRV, especially in the 1-min DBT test.
Springer
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