scholarly journals A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study (Preprint)

2020 ◽  
Author(s):  
Yung-Sheng Chen ◽  
Wan-An Lu ◽  
Jeffrey C Pagaduan ◽  
Cheng-Deng Kuo
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Low Frequency ◽  
Smartphone App ◽  
Spearman Correlation ◽  
Validity And Reliability ◽  
Limits Of Agreement ◽  
Short Term ◽  
Low Frequency Power ◽  
Frequency Power

BACKGROUND Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. OBJECTIVE The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. METHODS In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. RESULTS Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. CONCLUSIONS Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.

scholarly journals A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study

10.2196/18761 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18761
Author(s):  
Yung-Sheng Chen ◽  
Wan-An Lu ◽  
Jeffrey C Pagaduan ◽  
Cheng-Deng Kuo
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Low Frequency ◽  
Smartphone App ◽  
Spearman Correlation ◽  
Validity And Reliability ◽  
Limits Of Agreement ◽  
Short Term ◽  
Low Frequency Power ◽  
Frequency Power

Background Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.

Reproducibility of Heart Rate Variability Measures in Patients with Chronic Heart Failure

1996 ◽  
Vol 91 (4) ◽  
pp. 391-398 ◽  
Author(s):  
Piotr Ponikowski ◽  
Massimo Piepoli ◽  
Aham A. Amadi ◽  
Tuan Peng Chua ◽  
Derek Harrington ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Time Domain ◽  
Low Frequency ◽  
Variation Coefficient ◽  
Very Low Frequency ◽  
Low Frequency Power ◽  
Frequency Power

1. In patients with chronic heart failure, heart rate variability is reduced with relative preservation of very-low-frequency power (< 0.04 Hz). Heart rate variability has been measured without acceptable information on its stability and the optimal recording periods for enhancing this reproducibility. 2. To this aim and to establish the optimal length of recording for the evaluation of the very-low-frequency power, we analysed 40, 20, 10 and 5 min ECG recordings obtained on two separate occasions in 16 patients with chronic heart failure. The repeatability coefficient and the variation coefficient were calculated for the heart rate variability parameters, in the time-domain (mean RR, SDRR and pNN50), and in the frequency-domain: very low frequency (< 0.04 Hz), low frequency (0.04–0.15 Hz), high frequency (0.15–0.40 Hz), total power (0–0.5 Hz). 3. Mean RR remained virtually identical over time (variation coefficient 8%). The reproducibility of time-domain (variation coefficient 25–139%) and of spectral measures (variation coefficient 45–111%) was very low. The stability of the heart rate variability parameters was only apparently improved after square root and after log transformation. 4. Very-low-frequency values derived from 5 and 10 min intervals were significantly lower than those calculated from 40 and 20 min intervals (P < 0.005). Discrete very-low-frequency peaks were detected in 11 out of 16 patients on the first 40, 20 and 10 min recording, but only in seven out of 16 when 5 min segments were analysed. 5. The reproducibility of both time or frequency-domain measures of heart rate variability in patients with chronic heart failure may vary significantly. Square root or log-transformed parameters may be considered rather than absolute units in studies assessing the influence of management on heart rate variability profile. Recordings of at least 20 min in stable, controlled conditions are to be recommended to optimize signal acquisition in patients with chronic heart failure, if very-low-frequency power in particular is to be studied.

Preeclamptic pregnancy is associated with increased sympathetic and decreased parasympathetic control of HR

2000 ◽  
Vol 278 (4) ◽  
pp. H1269-H1273 ◽  
Author(s):  
Cheryl C. H. Yang ◽  
Te-Chang Chao ◽  
Terry B. J. Kuo ◽  
Chang-Sheng Yin ◽  
Hsing I. Chen
Keyword(s):  
Heart Rate ◽  
Low Frequency ◽  
Normal Pregnancy ◽  
Frequency Domain Analysis ◽  
Natural Logarithm ◽  
Sympathetic Regulation ◽  
History Of ◽  
Low Frequency Power ◽  
Normalized Units ◽  
Frequency Power

Previous work from our laboratory using heart rate variability (HRV) has demonstrated that women before menopause have a more dominant parasympathetic and less effective sympathetic regulations of heart rate compared with men. Because it is still not clear whether normal or preeclamptic pregnancy coincides with alternations in the autonomic functions, we evaluated the changes of HRV in 17 nonpregnant, 17 normotensive pregnant, and 11 preeclamptic women who were clinically diagnosed without history of diabetic neuropathy, cardiac arrhythmia, and other cardiovascular diseases. Frequency-domain analysis of short-term, stationary R-R intervals was performed to evaluate the total variance, low-frequency power (LF; 0.04–0.15 Hz), high-frequency power (HF; 0.15–0.40 Hz), ratio of LF to HF (LF/HF), and LF in normalized units (LF%). Natural logarithm transformation was applied to variance, LF, HF, and LF/HF for the adjustment of the skewness of distribution. We found that the normal pregnant group had a lower R-R value and HF but had a higher LF/HF and LF% compared with the nonpregnant group. The preeclamptic group had lower HF but higher LF/HF compared with either the normal pregnant or nonpregnant group. Our results suggest that normal pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of heart rate, and such alterations are enhanced in preeclamptic pregnancy.

Heart rate response and parasympathetic modulation during recovery from exercise in boys and men

2014 ◽  
Vol 39 (8) ◽  
pp. 969-975 ◽  
Author(s):  
Justin P. Guilkey ◽  
Matthew Overstreet ◽  
Bo Fernhall ◽  
Anthony D. Mahon
Keyword(s):  
Heart Rate ◽  
Maximal Exercise ◽  
Ventilatory Threshold ◽  
Heart Rate Recovery ◽  
Low Frequency ◽  
Cycle Ergometer ◽  
Submaximal Exercise ◽  
Frequency Domains ◽  
Low Frequency Power ◽  
Frequency Power

The purpose of this study was to examine the influence of postexercise parasympathetic modulation, measured by heart rate variability (HRV), on heart rate recovery (HRR) in boys (n = 13, 10.1 ± 0.8 years) and men (n = 13, 23.9 ± 1.5 years) following maximal and submaximal exercise. Subjects completed 10 min of supine rest, followed by graded exercise on a cycle ergometer to maximal effort. On a separate day, subjects exercised at an intensity equivalent to ventilatory threshold. Immediately following both exercise bouts, 1-min HRR was assessed in the supine position. HRV was analyzed under controlled breathing during the final 5 min of rest and recovery in the time and frequency domains and transformed to natural log (ln) values. Boys had a greater 1-min HRR than men following maximal (58 ± 8 vs. 47 ± 11 beats·min−1) and submaximal (59 ± 8 vs. 47 ± 15 beats·min−1) exercise (p < 0.05). Following maximal exercise, boys had greater ln root mean square successive differences in R-R intervals (2.52 ± 0.95 ms), ln standard deviation of NN intervals (3.34 ± 0.57 ms), ln high-frequency power (4.32 ± 2.00 ms2), and ln low-frequency power (4.98 ± 1.17 ms2) than men (1.33 ± 0.37 ms, 2.52 ± 0.24 ms, 1.32 ± 1.06 ms2 and 2.80 ± 0.74 ms2, respectively) (p < 0.05). There were no differences in any HRV variables between groups following submaximal exercise (p > 0.05). In conclusion, it appears that greater parasympathetic modulation accounts for greater HRR following maximal exercise in boys versus men. Although submaximal HRR was greater in boys, parasympathetic responses were similar between groups.

scholarly journals Heart Rate Variability-Derived Features Based on Deep Neural Network for Distinguishing Different Anaesthesia States

2020 ◽  
Author(s):  
Jian Zhan ◽  
Zuo-xi Wu ◽  
Zhen-xin Duan ◽  
Gui-ying Yang ◽  
Zhi-yong Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Deep Neural Network ◽  
Low Frequency ◽  
Support Vector ◽  
Low Frequency Power ◽  
Frequency Power

Abstract Background: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which used the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.Results: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.

Normalized Low Frequency Power of Heart Rate Variability Correlates Negatively With the SOFA Score in Severe Sepsis and Septic Shock

CHEST Journal ◽  
2011 ◽  
Vol 140 (4) ◽  
pp. 427A
Author(s):  
Subhasis Behera ◽  
Samuel Brown ◽  
Jason Jones ◽  
Michael Lanspa ◽  
Kathryn Kuttler ◽  
...  
Keyword(s):  
Heart Rate ◽  
Septic Shock ◽  
Heart Rate Variability ◽  
Severe Sepsis ◽  
Sofa Score ◽  
Low Frequency ◽  
Low Frequency Power ◽  
Sepsis And Septic Shock ◽  
Frequency Power

Supine low-frequency power of heart rate variability reflects baroreflex function, not cardiac sympathetic innervation

2009 ◽  
Vol 76 (4 suppl 2) ◽  
pp. S51-S59 ◽  
Author(s):  
Jeffrey P. Moak ◽  
David S. Goldstein ◽  
Basil A. Eldadah ◽  
Ahmed Saleem ◽  
Courtney Holmes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sympathetic Innervation ◽  
Low Frequency ◽  
Cardiac Sympathetic Innervation ◽  
Baroreflex Function ◽  
Low Frequency Power ◽  
Frequency Power

Effect of aging on gender differences in neural control of heart rate

1999 ◽  
Vol 277 (6) ◽  
pp. H2233-H2239 ◽  
Author(s):  
Terry B. J. Kuo ◽  
Tsann Lin ◽  
Cheryl C. H. Yang ◽  
Chia-Lin Li ◽  
Chieh-Fu Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Neural Control ◽  
Low Frequency ◽  
Frequency Domain Analysis ◽  
Middle Aged ◽  
Subsequent Aging ◽  
Sympathetic Regulation ◽  
Relative Measurements ◽  
Low Frequency Power ◽  
Frequency Power

To clarify the influence of gender on sympathetic and parasympathetic control of heart rate in middle-aged subjects and on the subsequent aging process, heart rate variability (HRV) was studied in normal populations of women ( n = 598) and men ( n = 472) ranging in age from 40 to 79 yr. These groups were divided into eight age strata at 5-yr intervals and were clinically diagnosed as having no hypertension, hypotension, diabetic neuropathy, or cardiac arrhythmia. Frequency-domain analysis of short-term, stationary R-R intervals was performed, which reveals very-low-frequency power (VLF; 0.003–0.04 Hz), low-frequency power (LF; 0.04–0.15 Hz), high-frequency power (HF; 0.15–0.40 Hz), the ratio of LF to HF (LF/HF), and LF and HF power in normalized units (LF% and HF%, respectively). The distribution of variance, VLF, LF, HF, and LF/HF exhibited acute skewness, which was adjusted by natural logarithmic transformation. Women had higher HF in the age strata from 40 to 49 yr, whereas men had higher LF% and LF/HF between 40 and 59 yr. No disparity in HRV measurements was found between the sexes in age strata ≥60 yr. Although absolute measurements of HRV (variance, VLF, LF, and HF) decreased linearly with age, no significant change in relative measurements (LF/HF, LF%, and HF%), especially in men, was detected until age 60 yr. We conclude that middle-aged women and men have a more dominant parasympathetic and sympathetic regulation of heart rate, respectively. The gender-related difference in parasympathetic regulation diminishes after age 50 yr, whereas a significant time delay for the disappearance of sympathetic dominance occurs in men.

Low-frequency component of the heart rate variability spectrum: a poor marker of sympathetic activity

1999 ◽  
Vol 276 (1) ◽  
pp. H215-H223 ◽  
Author(s):  
Melanie S. Houle ◽  
George E. Billman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Ventricular Fibrillation ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Frequency Component ◽  
Atropine Sulfate ◽  
Low Frequency Power ◽  
Heart Rate Variability Spectrum ◽  
Frequency Power

The low-frequency component of the heart rate variability spectrum (0.06–0.10 Hz) is often used as an accurate reflection of sympathetic activity. Therefore, interventions that enhance cardiac sympathetic drive, e.g., exercise and myocardial ischemia, should elicit increases in the low-frequency power. Furthermore, because an enhanced sympathetic activation has been linked to an increased propensity for malignant arrhythmias, one might also predict a greater low-frequency power in animals that are susceptible to ventricular fibrillation than in resistant animals. To test these hypotheses, a 2-min coronary occlusion was made during the last minute of exercise in 71 dogs with healed myocardial infarctions: 43 had ventricular fibrillation (susceptible) and 28 did not experience arrhythmias (resistant). Exercise or ischemia alone provoked significant heart rate increases in both groups of animals, with the largest increase in the susceptible animals. These heart rate increases were attenuated by β-adrenergic receptor blockade. Despite the sympathetically mediated increases in heart rate, the low-frequency power decreased, rather than increased, in both groups, with the largest decrease again in the susceptible animals: 4.0 ± 0.2 (susceptible) vs. 4.1 ± 0.2 ln ms2 (resistant) in preexercise control and 2.2 ± 0.2 (susceptible) vs. 2.9 ± 0.2 ln ms2 (resistant) at highest exercise level. In a similar manner the parasympathetic antagonist atropine sulfate elicited significant reductions in the low-frequency power. Although sympathetic nerve activity was not directly recorded, these data suggest that the low-frequency component of the heart rate power spectrum probably results from an interaction of the sympathetic and parasympathetic nervous systems and, as such, does not accurately reflect changes in the sympathetic activity.

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