Abstract— Major complications such as cardiac death and cardiac autonomic neuropathy are caused by diabetic autonomic neuropathy. Heart Rate Variability (HRV) analysis has shown to detect variations in the autonomic balance of heart rate and is useful for early detection of autonomic dysfunction. This study presents the outcome of HRV analysis of short ECG recordings taken from nondiabetic and type 2 diabetes patients, applying Poincaré plot indices represented by short term variation (SD1), long term variation (SD2) and complex correlation (CCM) measure which measures the temporal dynamics, for early detection of cardiac autonomic neuropathy. SD1 and the ratio SD1/SD2 were found to be significantly lower in type 2 diabetes patients than the control group. The highest discriminatory power was observed with CCM, indicating the advantage of using a dynamic measure for HRV rather than the static Poincaré plot indices. SD1 and CCM could be markers for CVD risk in type 2 diabetic patients.

I. INTRODUCTION

With 18.9% of its population being diabetic, the United Arab Emirates (UAE) is ranked 8th in the prevalence of type 2 diabetes (T2D) [1].

Diabetic autonomic neuropathy is a major complication of diabetes and includes cardiac autonomic neuropathy (CAN), uremic neuremia and peripheral neuropathy which can lead to sudden cardiac death (SCD). The traditional Ewing battery of tests for autonomic function, which document the presence of neuropathy, is not sensitive to preclinical or asymptomatic cardiac autonomic neuropathy [2, 3]. Heart Rate Variability (HRV) analysis is able to detect variations in autonomic modulation of the heart rate prior to clinical symptoms of CAN are identifiable [4, 5] and has been shown to be a useful assessment tool for diabetes associated cardiac dysfunction [2].

Standards of measurement and interpretation of HRV have been recommended by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology and include time and frequency domain as well as nonlinear measures (Tfesc/Naspe, 1996).

Current HRV parameters used in HRV analysis are essentially static measures of a time signal. To obtain a better

*Research supported by Khalifa University of Science, Technology and

Research Internal Research Fund. H. B Abubaker, H. S. Alsafar, H. F. Jelinek, K. A. Khalaf and A. H

Khandoker are with Khalifa University of Science, Technology and Research, Abu Dhabi, UAE (phone: +97124018158, e-mail: hanin.abubaker@kustar.ac.ae)

understanding of the relationship between autonomic control of the heart rate and diabetes a more dynamic measure of heart rate is required. The Poincaré Plot is a method proposed and utilized by Tulppo et al. (1996) for analysis of heart rate signals [3]. The Poincaré Plot allows determining linear components of the inter-beat variability associated with short and long term correlations of the signal (Tulppo et al., 1998; Karmakar et al., 2009a). An extension of the Poincaré Plot to determine and measure the temporal dynamics was proposed by Karmakar et al. (2011). The complex correlation method (CCM) relies on computing the beat-to-beat variation of the signal with increasing lag (beat intervals) rather than the global description the Poincaré plot, which provides indices based on n, and n + 1 beats. This study investigated whether the Poincare plot indices SD1, SD2 and CCM are able to differentiate between the beat-to-beat changes determined from the electrocardiogram (ECG) recordings of T2D and non-diabetic patients [6].

The rationale of the study is that cardiac autonomic neuropathy and its effect on cardiac function may be better and more accurately described and quantified using a measure that reflects the systems’ dynamics over the recording interval [6].

This aim of this study investigate whether the Poincaré plot indices SD1, SD2 and CCM are able to differentiate between the beat-to-beat changes associated with modulation by the autonomic nervous system from ECG recordings obtained from T2D patients and non-diabetic patients.

II. DATA COLLECTION

A total of 99 unrelated individual of UAE nationality were identified during their routine visit to hospital in Abu Dhabi, UAE and agreed to participate in this study. Of the total group 55 were diagnosed with T2D. Clinical assessment, questionnaire completion and clinical assessment of all participants were conducted to confirm their diagnosis with T2D. This study was performed with the approval of the Ethical Review Committee of Mafraq and Sheikh Khalifa Medical City hospitals in Abu Dhabi.

III. DATA ANALYSIS

A. Subjects and ECG signals The study included 99 people divided into two groups;

nondiabetic and T2D with sample size of 44 and 55 respectively (Females 65 % and males 35%). The average age of the entire population was (50.0 ±1.6) years. The population can be described as obese with average BMI of 31.05. Table 1 shows gender, age and BMI for each group.

Poincaré Plot Analysis of Heart Rate Variability in the Diabetic Patients in the UAE*

Hanin B. Abubaker, Habiba S. Alsafar, Herbert F. Jelinek, Member, IEEE, Kinda A. Khalaf, Member, IEEE, and Ahsan H. Khandoker, Senior Member, IEEE

2014 Middle East Conference on Biomedical Engineering (MECBME)February 17-20, 2014, Hilton Hotel, Doha, Qatar

978-1-4799-4799-7/14/$31.00 ©2014 IEEE 368

TABLE I. AGE, GENDER AND BMI CHARACTERISTIC OF STUDY POPULATION WITHIN THE TWO GROUPS

Gender Age (years) BMI

Nondiabetic 8M, 35F 42.0 ± 11.3 30.96 ± 6.76

Type 2 Diabetes 26M, 29F 58.2 ± 11.1 31.14 ± 7.35

P - value 0.003 <0.01 NS

The ECG signals were recorded over five minutes (ADInstruments, Sydney) and then edited with -beats to average- of 1 and R waves of at least 400 milliseconds apart. A 50 Hz filter was included to reduce the electrical noise in the system. Then the ECG signals were filtered with a digital low pass filter at 0.5 Hz and a digital high pass filter at 30 Hz using LabChart7 (version 7.3.1, ADInstruments). All ECG recordings were subjected to a manual check which led to elimination of four recordings in the nondiabetic patients due to noise > 5%. The R-wave-to-R-wave (RR) intervals for analyzing HRV features were determined using custom software in Matlab (Mathworks).

B. Statistical Analysis of HRV HRV results were expressed as means (± SD). These

results were tested for the null hypothesis that they come from a normal distributed population using Lilliefors test. The results suggested that the distribution was acceptable, although not exactly normal. The generalized linear model was applied (SPSS V20, IBM), which allowed for correcting for age and determining whether there was a differences between the nondiabetic and diabetic group when more than one HRV feature was determined [7]. The analysis set the age as the covariant; group as fixed factor and HRV features (SD1, SD2 SD1/SD2 and CCM) as the dependent variables. This is shown in Table 2.

C. Poincaré plot analysis The Poincaré plot is a popular two-dimensional

visualization tool for dynamic systems due to its intuitive display of the time series. It was generated as a scatter plot of instantaneous RR intervals against the immediately preceding RR intervals, shown in figure 1. The width (SD1) and the length (SD2) of the minor and major axes of Poincaré plot represent short and long-term variability of any nonlinear dynamic system as a static, global descriptive feature [8].

Figure 1. Top panels show RR interval of a nondiabetic sample (A) and its corresponding Poincaré plot (B). Bottom panels show RR interval of T2D (C) and its corresponding Poincaré plot (D).

CCM measures the point-to-point variation of the signal over time rather than gross description provided by the

Poincaré plot. It is computed in a windowed manner, which embeds the temporal information of the signal. A moving window of three consecutive points (RR intervals) from the Poincaré plot is considered and the temporal variation of the points is measured. If three points are aligned on a straight line then the value of the variation is zero, which represents the linear alignment of the points. Moreover, since the individual measure involves three points of the two dimensional Poincaré plot, it is comprised of at least four different points of the time series for lag m = 1 and at most six points in case of lag m ≥ 3. Hence the measure conveys information about four different lag correlations of the signal. If the Poincaré plot is composed of N points then the temporal variation of the plot, termed CCM, is composed of all overlapping three point windows and can be calculated as follows:

(1)

where m represents lag of Poincaré plot, A(i) represents the area of the i-th triangle and Cn is the normalizing constant which is defined as, Cn = π * SD1 * SD2, represents the area of the fitted ellipse over the Poincaré plot at lag-m. The length of major and minor axis of the ellipse are 2SD1, 2SD2, where SD1, SD2 are the dispersion perpendicular to the line of identity (minor axis) and along the line of identity (major axis) respectively. The detail mathematical formulation of CCM is reported in our previous study [9].

IV. RESULTS AND DISCUSSION

The principle of Poincaré plot construction is taken from nonlinear dynamics theory, but the derived indexes used for its quantification are essentially linear and fall within the time domain [3, 10]. Previously we developed mathematical formulations that relate each measure derived from the Poincaré plot geometry to well understood existing heart rate variability indexes [3].

Our results indicate that SD1 and SD1/SD2 did show significant differences between the groups. SD1 indicates short term variability, which has been shown to correlate to the high frequency component obtained from the Fast Fourier Transform, and hence indicates a reduction in the influence of the parasympathetic component of autonomic nervous system. The ratio SD1/SD2, which decreased, also reflects the change in ANS modulation of the heart rate as SD2 increased but not significantly. The dynamic temporal-based CCM measure was significantly (p<0.001) lower in T2D patients compared to control indicating that over the time of the recording, not only did the short term correlation between successive beats differ between the non-diabetic and diabetic groups, but the temporal variation was also significantly blunted. Heart rate is not constant and if it is, is a sign of impaired cardiac function, which carries with it an increased risk of fatal arrhythmia. Our study clearly indicates the decrease in the dynamic properties of the heart rate variability over time, which explains why people with diabetes are more prone to sudden cardiac death.

The results, shown in table 2, further substantiate findings of our previous study on CVD risk [11], where CCM was found to decrease with increased CVD risk as determined by the Framingham risk equation.

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Age correction resulted in SD1 loosing significance and SD2 becoming significant. This result indicates clearly the effect of aging on HRV; as short-term correlation are reduced with aging with long-term correlations increasing. CCM retained significance despite age correction suggesting that the difference between the two groups in the inherent temporal dynamics of the system are in part due to the presence of diabetes and the duration of diabetes.

V. CONCLUSION

SD1 is more sensitive to the influence of age and showed a non-significant decrease in the diabetic group. Long term variation (SD2) and the ratio SD1/SD2 indicated a significant difference between the two groups. SD2 therefore indicates a change in the sympathovagal balance favoring possibly sympathetic drive. The temporal dynamics (CCM) feature of HRV also reduced in T2D patients. The reduced CCM in T2D patients indicates that T2D patients are at higher risk of an adverse cardiac event as shown previously. In conclusion, the parasympathetic regulation is decreased and the dynamic HRV blunted as measured by CCM, which are strong indicators of increased CVD risk in T2D patients.

TABLE II. HRV ANALYSIS FOR NONDIABETIC AND TYPE 2 DIABETES

Feature (mean±SD)

p -value p-value w/

age correction Nondiabetic Type 2

SD1 (sec) 0.18 ± 0.15 0.12 ±0.20 0.0003 0.1140

SD2 (sec) 0.25 ± 0.20 0.41 ± 0.36 0.2744 0.0320

SD1/SD2 0.69 ± 0.20 0.43 ±0.38* 0.0013 0.0010

CCM 1.07 ± 0.68 0.60 ± 0.83* 3.9e-05 0.0120 Mean RR (sec) 0.86 ± 0.02 0.82 ± 0.02 0.2173 --

RMSSD RR (sec) 0.13 ± 0.02 0.08 ± 0.02 0.0870 --

SDNN RR (sec) 0.11 ± 0.01 0.07 ± 0.01* 0.0376 --

(*) p<0.05 between Non-diabetic and Type 2 Diabetes

ACKNOWLEDGMENT This study was supported by Khalifa University of

science, technology and research Internal Research Fund.

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