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Thousands of papers involved in heart rate variability (HRV). However, little was known about one important measure of HRV, the root mean square of successive heartbeat interval differences (RMSSDs). Another fundamental measure SDNN indicates standard deviation of normal to normal R-R intervals, where R is the peak of a QRS complex (heartbeat). Compared with SDNN, RMSSD is a short-term variation of heart rate. Through a time-frequency transformation, the ratio of low- and high-frequency power LF/HF represents the sympatho-vagal balance of the autonomic nervous system (ANS). Some research claimed that SDNN/RMSSD was a good surrogate for LF/HF. However, only two special cases supported this hypothesis in the literature survey. The first happened in resting supine state and the other was a group of prefrontal cortex patients. Both of their Pearson correlation coefficients reached 0.90, a reasonable criterion. In our study, a 6-week experiment was performed with 32 healthy young Asian males. The Pearson correlation coefficients had a normal distribution with average values smaller than 0.6 for 3 and 5-minute epochs, respectively. Our findings suggest this surrogate aspect could remain as a hypothesis.

RMSSD, the root mean square differences of successive R-R (heartbeat) intervals, is a significant indicator for both atrial fibrillation (AF) and sudden unexplained death in epilepsy (SUDEP) [

Measurements of HRV include time domain, frequency domain methods, and so on. They are noninvasive, as the tools to recognize the relationship between the autonomic nervous system (ANS) and cardiovascular mortality [

A standard routine of ECG signal processing.

Some research investigated the relationship between RMSSD and other important variables [

The rest of this paper is organized as follows. In Section

This section contains four parts. First is the experiment of ECG data collection. Second is the introduction of HRV parameters. Third is the introduction of correlation coefficient analysis. Finally, the whole data process is described.

Thirty-two healthy young male adults, whose ages ranged from 21 to 25 years (mean = 23 years), served as the subjects for this investigation. All of them were undergraduate and graduate students in National Chiao Tung University. The authors had obtained the informed consent of all subjects before the first experiment. The experimental apparatus consisted of a three-lead electrocardiograph (MSI E3-80, FDA 510(k) K071085) worked at 500 Hz sampling rate. The experiment was executed in the afternoon. Each subject sat in the chair first in a quiet room. They were all eyes closed during data recording. After the wires were fixed on the body, ECG signals were continuously recorded for the subsequent 20 minutes.

Each participant had to been recorded 6 times in 6 successive weeks. Due to some absences, only 17 subjects (53%) completed the 6-week experiment in time. There were 12 subjects (38%) in 7 weeks and 3 subjects (9%) in 8 weeks to complete the experiment. However, the data got from the 32 subjects was analyzed.

Many healthcare issues arose out of population aging [

Figure

The task force specified many different HRV metrics for both short-term records (5-minute) and long-term records (24-hour). Taking the reliability and accuracy of heart rate variability measurements into account [

To conduct the Fast Fourier Transform (FFT), an interpolation should be carried on first, since the RR interval time series is an irregularly time-sampled signal. This is not an issue in time domain analysis, but in the frequency domain analysis it has to be taken into account. If the spectrum estimate is calculated from this irregularly time-sampled signal, implicitly assuming it to be evenly sampled, additional harmonic components are generated in the spectrum. Therefore, the RR interval signal is usually interpolated before the spectral analysis to recover an evenly sampled signal from the irregularly sampled event series. The 4 Hz cubic spline interpolation was used in this study [

While the time domain measures help in assessing the magnitude of the temporal variations in the autonomically modulated cardiac rhythm, the frequency domain analysis provides the spectral composition of these variations. All frequency domain HRV metrics are based on the estimated power spectral density (PSD) of the NN (normal to normal RR) intervals. Although the task force [

Due to the simplicity of the algorithm (Fast-Fourier Transform) and high processing speed, non-parametric method, Welch method, was chosen to estimate the PSD [

The signal was split up into overlapped segments: the original data segment was split up into

The overlapping segments were then windowed by the Hamming window.

After doing the above instructions, the period gram was calculated by computing the FFT, and then computing the squared magnitude of the result. The individual period-grams were then time averaged, which reduced the variance of the individual power measurements. The end result was an array of power measurements versus frequency bin.

Through the use of computationally efficient algorithms such as FFT, the HRV signal was decomposed into its individual spectral components and their intensities, using PSD analysis. These spectral components were then grouped into three distinct bands: very low frequency (VLF), low frequency (LF), and high frequency (HF). The cumulative spectral power in the LF and HF bands and the ratio of these spectral powers (LF/HF) had demonstrable physiological relevance in healthy and disease states [

The physiological explanation of the VLF component (0.0033–0.04 Hz) is much less defined and the existence of a specific physiological process attributable to these heart period changes might even be questioned. The LF/HF power ratio is used as an index for assessing sympathovagal balance.

Given two jointly distributed random variables

Since the slope

Figure

Calculation of the Pearson correlation coefficient of SDNN/RMSSD and LF/HF. The period of sampled epoch is 5 minutes and the number of subjects is 32. The correlation coefficient

To avoid the instability of subjects in the beginning of the measurement, the head and tail of 20-minute ECG data were obsolete. For the 5-minute case, data from 2.5 min to 17.5 min was used. For the 3-minute case, data from 1 min to 19 min was used.

First, the values of 6 variables (SDNN, RMSSD, SDNN/RMSSD, LF, HF, and LF/HF) for both epoch cases were calculated by the in-house software. Each correlation coefficient was generated by 32 records of SDNN/RMSSD and 32 records of LF/HF. There were 36 coefficients for 3-minute epochs and 18 coefficients for 5-minute epochs, respectively. Hence, there were 3456 records of raw data totally, a huge number. However, to help the reader understand the results, a table (Table

HRV measurements of one subject.

01–04 | 04–07 | 07–10 | 10–13 | 13–16 | 16–19 | |
---|---|---|---|---|---|---|

Week 1 | ||||||

MH | 78 | 75 | 74 | 74 | 78 | 79 |

MR | 772 | 802 | 814 | 807 | 768 | 761 |

SD | 99 | 79 | 92 | 107 | 108 | 105 |

RM | 46 | 37 | 46 | 47 | 47 | 48 |

| ||||||

S/R | 2.2 | 2.1 | 2.0 | 2.3 | 2.3 | 2.2 |

| ||||||

LF | 1731.2 | 1383.5 | 2087.4 | 1991.2 | 2335.9 | 3131.6 |

HF | 229.1 | 193.3 | 317.0 | 436.9 | 409.7 | 511.8 |

| ||||||

L/H | 7.6 | 7.2 | 6.6 | 4.6 | 5.7 | 6.1 |

| ||||||

Week 2 | ||||||

MH | 95 | 95 | 98 | 97 | 99 | 96 |

MR | 631 | 629 | 613 | 618 | 604 | 628 |

SD | 54 | 50 | 49 | 47 | 56 | 70 |

RM | 34 | 28 | 24 | 25 | 23 | 32 |

| ||||||

S/R | 1.6 | 1.8 | 2.0 | 1.9 | 2.4 | 2.2 |

| ||||||

LF | 937.9 | 1149.5 | 704.6 | 548.6 | 752.6 | 787.4 |

HF | 131.3 | 155.6 | 192.5 | 202.9 | 101.4 | 161.9 |

| ||||||

L/H | 7.1 | 7.4 | 3.7 | 2.7 | 7.4 | 4.9 |

| ||||||

Week 3 | ||||||

MH | 102 | 102 | 99 | 99 | 95 | 100 |

MR | 588 | 590 | 605 | 609 | 630 | 598 |

SD | 46 | 51 | 48 | 49 | 53 | 54 |

RM | 17 | 20 | 19 | 21 | 25 | 20 |

| ||||||

S/R | 2.7 | 2.6 | 2.5 | 2.3 | 2.1 | 2.7 |

| ||||||

LF | 472.8 | 630.6 | 342.2 | 499.0 | 709.0 | 623.7 |

HF | 58.3 | 121.0 | 110.1 | 198.3 | 141.3 | 106.9 |

| ||||||

L/H | 8.1 | 5.2 | 3.1 | 2.5 | 5.0 | 5.8 |

| ||||||

Week 4 | ||||||

MH | 83 | 88 | 88 | 85 | 78 | 77 |

MR | 719 | 680 | 685 | 705 | 766 | 775 |

SD | 93 | 65 | 85 | 83 | 107 | 90 |

RM | 43 | 31 | 40 | 37 | 45 | 44 |

| ||||||

S/R | 2.2 | 2.1 | 2.1 | 2.2 | 2.4 | 2.0 |

| ||||||

LF | 2022.5 | 587.9 | 1601.3 | 1650.0 | 2215.5 | 1268.7 |

HF | 296.2 | 219.7 | 247.1 | 374.5 | 381.6 | 325.9 |

| ||||||

L/H | 6.8 | 2.7 | 6.5 | 4.4 | 5.8 | 3.9 |

| ||||||

Week 5 | ||||||

MH | 77 | 75 | 78 | 76 | 74 | 72 |

MR | 777 | 806 | 766 | 786 | 813 | 830 |

SD | 97 | 90 | 85 | 105 | 95 | 102 |

RM | 47 | 52 | 48 | 61 | 50 | 64 |

| ||||||

S/R | 2.1 | 1.7 | 1.8 | 1.7 | 1.9 | 1.6 |

| ||||||

LF | 1924.5 | 1300.2 | 936.2 | 2771.7 | 1988.1 | 2188.6 |

HF | 339.7 | 333.7 | 353.1 | 889.7 | 356.7 | 940.5 |

| ||||||

L/H | 5.7 | 3.9 | 2.7 | 3.1 | 5.6 | 2.3 |

| ||||||

Week 6 | ||||||

MH | 93 | 90 | 86 | 90 | 85 | 82 |

MR | 643 | 671 | 700 | 668 | 705 | 731 |

SD | 54 | 66 | 101 | 76 | 93 | 97 |

RM | 28 | 37 | 50 | 38 | 48 | 55 |

| ||||||

S/R | 1.9 | 1.8 | 2.0 | 2.0 | 1.9 | 1.8 |

| ||||||

LF | 833.0 | 1392.1 | 2269.1 | 1842.2 | 1356.6 | 1736.3 |

HF | 193.3 | 300.5 | 372.3 | 217.3 | 579.0 | 601.2 |

| ||||||

L/H | 4.3 | 4.6 | 6.1 | 8.5 | 2.3 | 2.9 |

MH: mean heart rate (/m), MR: mean RR intervals (ms), SD: standard deviation of normal to normal RR intervals, RM: root mean square of normal to normal RR intervals, S/R: ratio of SDNN and RMSSD, LF: low-frequency power (ms^{2}), HF: high-frequency power (ms^{2}), L/H: ratio of LF and HF, 01–04: epoch from 1 minute to 4 minute within the time sequence.

Second, the main outcomes, correlation coefficients of SDNN/RMSSD and LF/HF were calculated, as shown in Figure

The results of this work included 5 figures, from Figure

Pearson correlation coefficients of 6 weeks for 5-minute epochs.

Pearson correlation coefficients of 6 weeks for 3-minute epochs.

Normal distribution of Pearson correlation coefficients for 5-minute and 3-minute epochs. The

The trend of Pearson correlation coefficients with 5-minute epochs during 6 weeks.

The trend of Pearson correlation coefficients with 3-minute epochs during 6 weeks.

In Figure

After calculating the mean and the standard deviation, the correlation coefficients can be observed in normal distribution, referring to (

The value of first week in Figure

There are three aspects in this section. The first aspect is the epoch effect of various lengths. The second aspect is the reliability of HRV variables. The final aspect is whether SDNN/RMSSD is a proper surrogate for LF/HF.

Recent literature on HRV pointed up the relationship between variables and epoch lengths. Among these variables, RMSSD and HF were more reliable than other metrics for various epochs [

Above findings revealed the stability of RMSSD and HF in various epochs, but SDNN and LF remained to be discussed. For the epoch effect, we are interested in SDNN/RMSSD and LF/HF. The correlation coefficients of SDNN/RMSSD and LF/HF were calculated in our work. In Figure

Reliability is a synonym of reproducibility and stability in the following research [

The following four experiments were conducted in different days. The days between first and last experiment were 2 days [

Following above findings, LF and HF were suggested to be reliable, but LF/HF was not [

The main point of this paper is to study the relationship between SDNN/RMSSD and LF/HF. There were two publications involved in this topic [

In the first work, there were 14 healthy young subjects. The physiological parameters were measures for 10 minutes of supine state and 10 minutes after 70° upright tilt test (HUT). After the experiment, the HRV characteristics were calculated and the correlation coefficients of SDNN/RMSSD and LF/HF were 0.90 and 0.63, respectively, [

In second work, there were 29 participants (7 healthy controls and 22 brain injury patients). Eight of patients were prefrontal cortex patients. The experimental procedures included sitting, standing, preparation stage 1, preparation stage 2, mental task 1, and mental task 2. For all 29 subjects, the correlation coefficients were 0.57, 0.63, 0.57, 0.78, 0.42, and 0.64. For 22 brain injury patients, the correlation coefficients were 0.62, 0.68, 0.57, 0.80, 0.49, and 0.70. For 8 prefrontal cortex patients, the correlation coefficients were 0.76, 0.78, 0.90, 0.93, 0.94, and 0.93, respectively, [

In our work, there were 32 young male subjects included. All the subjects sat quietly with eye closed in the whole experiment. The 6 trials had proceeded in 6 successive weeks. The results were observed by 3-minute epoch and 5-minute epoch, respectively, as listed in Figures

Whether SDNN/RMSSD is a proper surrogate for LF/HF, a criterion (

The authors thank Mr. Shih-Shiang Lin, Mr. Yaw-Chern Lee, Mr. Wen-Chih Zhang, and Mr. Yi-Sen Shih for their support in both program design and data analysis.

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