Australas Phys Eng Sci Med DOI 10.1007/s13246-014-0281-x

SCIENTIFIC NOTE

Robustness evaluation of heart rate variability measures for age gender related autonomic changes in healthy volunteers Guanzheng Liu • Qian Wang • ShiXiong Chen • GuangMin Zhou • WenHui Chen • YuanYu Wu

Received: 2 August 2013 / Accepted: 26 May 2014  Australasian College of Physical Scientists and Engineers in Medicine 2014

Abstract To analyze motion artifact’s affect on HRV measures, the age/gender related autonomic changes were investigated by using different HRV measures from wearable medical devices under ambulatory home-monitoring condition. Twelve healthy undergraduates and 20 healthy elderly subjects participated in the research. The electrocardiogram data was collected by using waist-worn device developed by us. Ten HRV measures were used to analyze the age-related automatic change including linear and nonlinear HRV indexes. Many linear HRV indexes were seriously contaminated by motion artefact, and did not reflect the age-related autonomic change. The approximate entropy (p \ 0.001) was the best indicator among 10 HRV indexes. However, the approximate entropy was also contaminated by motion artefact and did not reflect the gender-related autonomic change. The study verified the hypothesis that the HRV measures could be contaminated under ambulatory monitoring condition. It is importance for ambulatory home-monitoring to study the robustness of HRV measures. Keywords HRV  Robustness  Ambulatory monitoring  Motion artefact

G. Liu (&)  Q. Wang  G. Zhou  W. Chen  Y. Wu Biomedical Engineering Program, Sun Yat-sen University, Guangzhou, China e-mail: [email protected] S. Chen Jiu Jiang No. 1 People’s Hospital, Jiujiang, China

Introduction Over the last few decades, we have witnessed the recognition of a significant relationship between the autonomic nervous system and various pathophysiologies. As a noninvasive research and clinical tool, HRV analysis is widely used to investigate the cardiac and autonomic nervous system. The abnormal value of HRV, which reflects autonomic deregulation, has been shown to be an independent predictor of mortality in various patient populations [1]. The depressed heart rate variability is as an independent risk factor for a poor prognosis of chronic congestive heart failure (CHF) [2]. Sudhir et al. [3] suggested the increased risk of early mortality is associated with reduced HRV in acute myocardial infarction patients. Schroeder et al. [4] found that there was a decline in HRV relatively early in the development of hypertension. The prospective results also indicated that decreases in autonomic nervous function preceded the development of clinical hypertension. The study found that time domain parameters of HRV were significantly lower in the patients with thalassaemia major than the health people [5]. The magnitude and complexity of HRV were reduced in young patients with diabetes mellitus (DM), which indicating vagal dysfunction [6]. Several investigators have also reported reduced HRV in various healthy and diseased populations, such as obese subjects [7], healthy smokers [8], people with stressful conditions [9], overworked subjects [10] and so on. So essentially, the quantification of HRV has been verified as an improved diagnostic tool for cardiovascular autonomic dysfunction. In another side of the spectrum, HRV measures can be computed by using traditional time and frequency domain methods as well as nonlinear methods. Time domain indexes of HRV are the first used indexes and simplest way

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to calculate statistically consecutive RR intervals, including standard deviation of all RR intervals (SDNN), square root of mean of sum of squares of differences between adjacent RR interval (RMSSD), the percent of differences of adjacent RR intervals [50 ms (pNN50%) [11]. Frequency domain indexes are more elaborated indexes based on spectral analysis, including of very low frequency component (VLF: 0–0.04 Hz), a low frequency component (LF: 0.04–0.15 Hz), a high frequency component (HF: 0.15–0.4 Hz), The LF/HF ratio, and total power (TP: 0–0.4 Hz) [12]. Nonlinear phenomena are certainly involved in the genesis of HRV. Recently, various nonlinear indexes are also used to assess the complexes, including fractal dimension (FD) [13], Lyapunov exponents [14], approximate entropy (ApEn) [15], sample entropy (SampEn) [16], etc. Current research primarily focuses on the exploration of HRV-analysis methods and clinical applications; however, little attention is being paid to reliability and robustness of HRV analysis. In fact, the most salient feature of HRV is its spontaneous fluctuation, even if the environmental parameters are maintained constant and no perturbing can be identified [17]. Moreover, HRV parameters could be affected by motion artefact s which can be produced by wearable medical devices and ubiquitous health intervention services during ambulatory monitoring condition [18, 19]. In addition, some researchers also suggested that the analysis of HRV could be affected by gender and age differences [20, 21], ambient temperature and air pollution [22] etc. Thus, whether analysis of HRV could be recommended in this risk stratification for better management of various patients’ needs further investigation [2]. In order to verify this motion artefact’s affect on HRV measures, the paper mainly investigates age- and genderrelated autonomic changes by using HRV measures under ambulatory home-monitoring condition. To obtain the reliable and robustness HRV measures, 10 classical linear and nonlinear HRV indexes (Table 1) were used to assess the age- and gender-related autonomic nervous changes.

Table 1 Robustness evaluation of different HRV indexes between elderly group and student group Indexes

Elderly group (mean ± SD)

Student group (mean ± SD)

Significance parameter (p value)

Time domain SDNN

0.80 ± 0.38

1.00 ± 0.71

0.39

rMSSD

0.67 ± 0.26

1.00 ± 0.50

0.05

pNN50%

0.83 ± 0.55

1.00 ± 0.67

0.42

CVrr

0.39 ± 0.44

1.00 ± 0.86

0.04

Frequency domain LF

0.74 ± 0.24

1.00 ± 0.53

0.13

HF

0.69 ± 0.21

1.00 ± 0.53

0.08

TP

0.83 ± 0.26

1.00 ± 0.40

0.20

LF/HF

1.06 ± 0.07

1.00 ± 0.08

0.03

Entropy ApEn

0.92 ± 0.04

1.00 ± 0.07

0.001

SampEn

0.91 ± 0.05

1.00 ± 0.07

0.003

Bold values if significant difference with the reference value at p [ 0.05

over 5 min), RMSSD (root mean square of the successive normal sinus RR intervals difference over 5 min), CVrr (the coefficient of variation of R–R interval) and pNN50% (percentage of successive normal sinus RR intervals longer than 50 ms during 5 min). The frequent domain indexes

Method and experiment

The nonparametric power spectral density (PSD) analysis was used to obtain smooth power spectrum curve based on fast Fourier transform (FFT) [23]. Then, three main spectral components are distinguished in a spectrum calculated from short-term recordings of 5 min. The three main frequency components included very low frequency component (VLF: 0–0.04 Hz), low frequency component (LF: 0.04–0.15 Hz), and high frequency component (HF: 0.15–0.4 Hz). The representative frequent domain parameters were selected to analyze the differences of HRV, as following: TP (frequency component: 0–0.4 Hz), LF, HF, and LF/HF ratio.

HRV indexes

Approximate and sample entropy indexes

The HRV indexes were computed as the follows.

Entropy is a tendency for isolated systems in nature to move from order to disorder. Entropy, as it relates to dynamical systems, is the rate of information production. The approximate entropy (ApEn) is becoming a usual tool for characterizing the RR interval series [15]. The ApEn was computed based on the following Eqs. (1)–(5). First, it assume that RR time series is the data set {RR (k), k = 1, 2,…,N} with length N. Then, the phase space is

The time domain indexes Four representative time domain parameters were selected to analyze the differences of HRV between healthy elderly subjects and healthy graduate students, as following: SDNN (standard deviation of all normal sinus RR intervals

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reconstructed by choosing two parameters: the embedding dimension (m) and the delay (s) [24]. The delay (s) is 1 beat and embedding dimension (m) is 2 in the paper. The templates (N - m ? 1) are composed as follows:   X m ðiÞ ¼ RRðiÞ; RRði þ sÞ; . . .; RRði þ m  1ÞÞ ð1Þ 8i 2 f1; N  ðm  1Þg The recurrence matrix and distance between matrixes are calculated as Eqs. (2) and (3):   1 if . . .dðX m ðiÞ; X m ðjÞÞ  e ð2Þ covði; jÞ ¼ 0; f . . .dðX m ðiÞ; X m ðjÞÞ [ e vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi um1 uX m m ð3Þ ðRRðk þ iÞ  Rðk þ jÞÞ2 ; dðX ðiÞ; X ðjÞÞ ¼ t k¼0

where e is constant (e.g. SDNN). Thus, the approximate entropy is defined as:  mþ1  PNmþ1  Npm ðiÞ  PNm Np ðiÞ log Nmþ1 i¼1 i¼1 log Nm ApEnðm; eÞ ¼  N mþ1 Nm ð4Þ Then, for each i: Npm ðiÞ ¼

Nmþ1 X

ð5Þ

covði; jÞ

j¼1

The approximate entropy indicates more similarity by itself matching present for finite time series [15]. To reduce the bias caused by itself matching, sample entropy (SampEn) was developed to quantify heart rate variability [16]. Equations (2) and (4) were adjusted: 8 9 < 1; if . . .dðX m ðiÞ; X m ðjÞÞ  e i 6¼ j = covði; jÞ ¼ 0; if . . .dðX m ðiÞ; X m ðjÞÞ [ e i 6¼ j ð6Þ : ; 1; if . . .i ¼ j Then the SampEn was computed as following equation: PNmþ1 SampEnðm; eÞ ¼ ln

N m ðiÞ

p ðNmþ1 Þ  N mþ1

i¼1

PNm

N mþ1 ðiÞ

p i¼1 ð Nm Þ Nm

!

Elderly cohort. The 20 healthy elderly subjects were divided into two groups: 10 elderly men with a mean age of 67.8 ± 5.20 years (range 59–76 years) and 10 elderly women with a mean age of 63.8 ± 3.29 years (range 58–70 years, 10 male, 10 female). The age between old men group and old women group had not significantly difference (p [ 0.05). All procedures were carried out while the participants were seated in comfortable chair. None of the participants reported having a history of psychiatric or neurological disorders. All participants were also asked to refrain from alcohol, cigarette smoking and caffeine-containing beverages as well as relaxed condition for 24 h. All examinations were performed under the regulations of our Institutional ethnic committee. Electrocardiogram data collection Continuous ambulatory ECG monitoring was conducted repeatedly on each volunteer by using the waist-worn ECG device [18]. The sampling rate for ECG signals was set to be at 200 samples per second. A 50 Hz notch filter, a 0.5 Hz high-pass filter frequency and a 33 Hz low-pass filter frequency were designed to filter various interference, including motion artefact, power frequency, baseline drift and electrode contact. And then, the second order (0.5–20 Hz) band pass filter, smoothing filter and initial threshold method were used to detect exactly RR intervals. Statistical analysis We used SPSS v17.0 for all of our statistical tests. Mean and standard deviations were used to evaluate the mean absolute error between the elderly group and the student group. Where two datasets were compared, we performed two sample t-tests for each individual method. The robustness of indexes was assessed based on the significance parameter (p value). The smaller significance parameter (p value) is, the stronger the robustness is. The significance level chosen was a = 0.05.

ð7Þ Experiment procedures

Results

Twelve healthy undergraduates with a mean age of 25.9 ± 2.71 years (range 22–32 years, 8 male, 4 female) and 20 healthy elderly subjects with a mean age of 65.8 ± 4.7 years (range 58–76 years, 10 male, 10 female) have a rest for 5 min in a comfortable chair; then, wear the waist-worn ECG device; at last, start monitoring ECG for 5 min with minor body motion. A recording from this whole cohort was used to analyze age-related difference. The Gender-related difference was analyzed from the

Considered that there was also difference of different indexes, we report on indexes grouped in time-domain (e.g. SDNN, CVrr, pNN50% and rMSSD), frequency-domain (e.g. LF, HF, TP and LF/HF) and information entropy (ApEn and SampEn) categories. Then, robustness was discussed among the representativeness indexes from three HRV analysis methods. To assess the robustness among different HRV indexes, all HRV measures were adjusted by the following Eq. (8):

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Australas Phys Eng Sci Med Fig. 1 The time-domain indexes of heart rate variability indexed to student group, including rMSSD and CVrr where significantly difference (asterisks). Bars represent SD

index ðRindex subject Þnorm ¼ Rsubject =Mean ! N X index Mean ¼ Mean Rstudent ;

ð8Þ ð9Þ

1

where, the parameter N is total numbers of student group. Mean means the average value of student group for each   index. The parameter Rindex means the real value of subject    each index. The parameter Rindex means the subject norm

normalized value of each index. The significance level chosen was a = 0.05. Age-related HRV difference Time-domain indexes difference The time-domain indexes (indexed to the student group) difference of HRV analysis is demonstrated in Fig. 1. Furthermore, in the elderly group, the SDNN (0.80 ± 0.38 vs. 1.00 ± 0.71), the pNN50% (0.83 ± 0.55 vs. 1.00 ± 0.67), the rMSSD (0.67 ± 0.26 vs. 1.00 ± 0.50), the CVrr (0.39 ± 0.44 vs. 1.00 ± 0.86) were lower than those in student group. Statistical testing shows that CVrr and rMSSD had significant differences between elderly group and student group with the significant parameters lower than 0.05. However, the SDNN and pNN50% had not significant differences between elderly group and student group. The significance level of SDNN and pNN50% is 0.39, 0.42, respectively. In addition, the one standard deviation of elderly group was lower than one of student group. Thus, the statistical testing suggested autonomic function changes in the elderly. However, only CVrr and rMSSD indexes could significantly reflect the age-related autonomic nervous change.

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Frequency-domain indexes difference The frequency-domain indexes (indexed to the student group) difference of HRV analysis is demonstrated in Fig. 2. Furthermore, in the elderly group, the LF power (0.74 ± 0.24 vs. 1.00 ± 0.53), the HF power (0.69 ± 0.21 vs. 1.00 ± 0.53), and the TP power (0.83 ± 0.26 vs. 1.00 ± 0.40) were lower than those in student group. Statistical testing shows that those indexes had not significant differences between elderly group and student group with the significant parameters higher than 0.05. However, the LF/HF ratio of elderly group (1.06 ± 0.07 vs. 1.00 ± 0.08) was significant higher than one of student group with the significant level (a \ 0.03). In addition, the one standard deviation of LF/HF ratio was obvious lower than those of LF power, HF power and TP power. Thus, the statistical testing suggested autonomic function changes in the elderly. Only, the LF/HF ratio from frequency-domain indexes could significantly reflect the age-related autonomic nervous change. Information entropy indexes difference The information entropy indexes (indexed to the student group) difference of HRV analysis is demonstrated in Fig. 3. Furthermore, in the elderly group, the approximate entropy (0.92 ± 0.04 vs. 1.00 ± 0.07), sample entropy (0.91 ± 0.05 vs. 1.00 ± 0.07) were lower than those in student group. Statistical testing shows that those indexes had significant differences between elderly group and student group. The significant parameter of ApEn (0.001 vs. 0.003) was lower than one of SampEn. In addition, for ApEn and SampEn, the one standard deviation was obviously lower than the average. Thus, the statistical testing suggested autonomic function changes in the elderly. Both

Australas Phys Eng Sci Med Fig. 2 The frequency-domain indexes of heart rate variability indexed to student group, including LF/HF where significantly difference (asterisks). Bars represent SD

Fig. 3 The information entropy indexes of heart rate variability indexed to student group, both ApEn and SampEn in the elderly group was significantly higher. Bars represent SD

Fig. 4 The information entropy indexes of heart rate variability indexed to student group, including ApEn and SampEn in the elderly group was not significantly higher. Bars represent SD

ApEn and SampEn indexes could significantly reflect the age-related autonomic nervous changes.

robustness of approximation entropy was strongest. It could more effectively reveal the age-related autonomic difference. Thus, the information entropy had obviously better robustness than conventional time and frequency indexes with the smallest significant parameter (p value).

Robustness of the HRV indexes Table 1 lists the age-related autonomic nervous change based on different HRV indexes, including time-domain indexes, frequency-domain indexes and information entropy. The result demonstrated most of the time domain and frequency domain indexes hadn’t significantly changed between elderly group and student group, such as SDNN, rMSSD, LF, HF and TP. Only, three linear HRV indexes had significantly changed between elderly group and student group, such as pNN50%, CVrr and LF/HF. The information entropy (e.g. ApEn and SampEn) had significantly changed between elderly group and student group. The robustness among different HRV indexes was revealed by the significance parameter (p value). The significance parameters of approximation entropy (p \ 0.001) was obvious smaller than those of other indexes. Thus, the

Gender-related HRV difference The above-mentioned result (‘‘Age-related HRV difference’’ section) demonstrated the information entropy is the best HRV analysis method. Thus, the gender-related difference was discussed by using the SampEn AND ApEn. Figure 4 demonstrated the gender-related difference by using information entropy indexes (e.g. ApEn and SampEn). Furthermore, in the elderly men, the approximate entropy (2.19 ± 0.07 vs. 2.24 ± 0.12), sample entropy (2.15 ± 0.06 vs. 2.24 ± 0.06) were slight lower than those in elderly women. The significance parameter (p value) of ApEn and SampEn between elderly men group and elderly

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women group was 0.407, 0.438, respectively. Thus, the gender-related autonomic change was not significantly found by using information entropy HRV indexes.

Discussion and conclusion Discussion As a non-invasive marker, HRV is a widely used to forecast the risk of cardiovascular diseases [25]. In fact, various factors could affect HRV value to some degree. The different HRV indexes was compared and discussed in several literatures. Neumann et al. [26] investigated the impact of cerebral function on linear and non-linear dynamics in heart rate variability. The study found these indexes of linear and non-linear were significantly reduced in donors. Guzzetti et al. [27] evaluated spectral and non-linear (steeper 1/f slope) analysis of 24-h HRV in CHF patients. The result suggested that the reduction of LF power (p \ 0.01) seems the best indicator among those considered. Other researchers also found some difference of different HRV indexes in sedated cardiac surgery [28]. With the rapid development of monitoring technology (e.g. wireless communication and sensor), some customized ECG monitoring systems were emerging in recent years. For example, Gao et al. [29] designed and tested a smartphonesbased multi-lead ECG monitoring system. We also designed a waist-worn ECG device [18]. The customized ECG monitoring system could achieve long-term ECG monitoring with minor body motion under ambulatory home-monitoring condition. However, the ECG data was contaminated in the presence of motion artefact. Therefore, some R-wave and RR-interval may be missed. The contaminated RR-interval data must influence all HRV measures to various degrees. For example, Let us suppose there are one hundred and one RR intervals as following: RR1 ¼ R2  R1 ; RR2 ¼ R3  R2 ; RR3 ¼ R4  R3 ; . . .; RR101 ¼ R102  R101 ; where R1, R2,…,R102 were R wave. The difference of adjacent RR intervals (RR1 and RR2) is lower than 50 ms. Supposing The number of differences of adjacent RR intervals [50 ms is 10. The percent of differences of adjacent RR intervals[50 ms (pNN50%) is 10 %. If one R wave (e.g. R2) was missing, the RR intervals become to: RR1 ¼ R3  R1 ; RR2 ¼ R4  R3 ; RR3 ¼ R4  R3 ; . . .; RR100 ¼ R102  R101 ; where the difference of adjacent RR intervals (RR1 and RR2) is often greater than 50 ms. Thus, the pNN50% was calculated as 11.1 % (11/99).

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The missing RR data also influences entropy analysis. Kim et al. [30] found some nonlinear HRV parameters (e.g. distended fluctuation) may not be appropriate for the accurate HRV analysis with missing RR interval data. Then, age- and gender-related autonomic nerve had been widely discussed by using different HRV measures. For example, Umetani et al. [21] had found the age- and gender-related autonomic nerve difference by using time domain, including SDNN, rMSSD and pNN50. The result demonstrated all time-domain indexes of healthy subjects declined with aging. And, Gender differences decreased at age [ 30 years and disappeared at age [50 years. Vigo et al. [31] found age-related changes of heart rate variability within independent frequency components. Jokinen et al. [32] also showed age-related autonomic nervous changes within some non-linear indexes such as spectral, fractal and complexity characteristics. Stein et al. [33] studied the differing effects of age on HRV in men and women by using conventional time and frequency domain indexes. Moodithaya et al. [34] explored the influence of gender on age-related changes in cardiac autonomic regulation. The HF/LF ratio was significantly higher in the adolescent and adult females compared to male of these age groups. Most of the research is focused on the relationship between age/gender and several HRV measures. In the paper, we mainly aimed to analyze the reliability and robustness of different HRV measures which was used to assess age-related autonomic nerve change. The robustness and reliability of HRV measures denotes the ability (HRV measures differentiate the aging effect) resists change without adapting HRV measures in the presence of motion artefact. Furthermore, we found the best HRV measure among 10 classical indexes (SDNN, pNN50%, rMSSD, CVrr, LF, HF, LF/HF, TP, ApEn and SampEn). Our result confirmed the age-related autonomic change. However, we also found that part of HRV indexes were seriously contaminated so that the age-related autonomic nerve change wasn’t reflected. The gender-related autonomic nerve change wasn’t revealed in the study. The possible reason was that gender differences disappeared at age [50 years [21]. In addition, the important fact that the age-/gender differences was small, has contributed to analyze the effect of movement artefact on HRV measures. Conclusion In our paper, we mainly compared the different HRV indexes for age- and gender-related autonomic changes by using wearable medical device that we developed [18]. To investigate ambulatory monitoring affect on HRV measures, the age- and gender-related autonomic nervous changes were assessed by using time-domain, frequency-

Australas Phys Eng Sci Med

domain and information entropy methods. The conclusions to this study are as follows: (1)

(2)

(3)

Many linear HRV indexes were seriously contaminated and did not reflect the age-related autonomic change (e.g. SDNN, rMSSD, LF, HF and TP). Only three linear HRV indexes still reflect the age-related autonomic change (e.g. LF/HF, CVrr and pNN50%). The robustness of approximate entropy (p \ 0.001) is best. It can discriminate age-related autonomic change. Gender-related autonomic nervous change may be smaller than age-related change. The approximate entropy was also seriously contaminated by move artefact, and did not reflect the gender-related autonomic nervous change.

Thus, this low population study supported the hypothesis that the HRV measures could be seriously contaminated by wearable medical devices. It is importance for ambulatory home-monitoring to study the robustness HRV measures. Furthermore, the specific explanatory power of HRV parameters is less well-established in comparison to sophisticated cardiac investigations. Acknowledgments These authors are sincerely thankful to all volunteers participated in the experiment.

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