Linear and non-linear analysis of cardiac health in diabetic subjects

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Abstract

Diabetes is a chronic disease characterized by hyperglycaemia, which leads to specific long-term complications: retinopathy, neuropathy, nephropathy and cardiomyopathy. Analysis of cardiac health using heart rate variation (HRV) has become a popular method to assess the activities of the autonomic nervous system (ANS). It is beneficial in the assessment of cardiac abnormalities, because of its ability to capture fast fluctuations that may be an indication of sympathetic and vagal activity.

This paper documents work on the analysis of both normal and diabetic heart rate signals using time domain, frequency domain and nonlinear techniques. The study is based on data from 15 patients with diabetes and 15 healthy volunteers. Our results show that non-linear analysis of HRV is superior compared to time and frequency methods. Non-linear parameters namely,correlation dimension (CD), approximate entropy (ApEn), sample entropy (SampEn) and recurrence plot properties (REC and DET), are clinically significant.

Introduction

Diabetes mellitus, or diabetes, is a chronic disease, which is characterized by hyperglycaemia. Hyperglycaemia, is a metabolic disorder, were excess glucose is present in the blood. This results in an elevated blood glucose level, which leads to serious detrimental consequences. The disease affects eye (retinopathy) [1], [2], nerves (neuropathy) [3], kidney (nephropathy) [4] and heart (cardiomyopathy) [5].

According to World Health Organization (WHO) more than 220 million people worldwide had diabetes, in 2009. It is estimated that this figure will increase to 440 million by the year 2030 [6]. In 2008, the American Diabetes Association reported 23.6 million (approximately 7.8% of the population) children and adults in the United States have diabetes [7].

Cardiovascular disease (CVDs) is the number one cause of death globally. WHO estimated about 29% of deaths were due to CVDs (totaling 17.1 million) in 2004. They projected about 23.6 million people will succumb to the disease by 2030 [8].

Heart rate variation (HRV) is the name of a biological time series signal which indicates the variation of heart rate between two consecutive heart beats [9]. HRV is a non-invasive tool to assess the autonomic nervous system (ANS). HRV may take precedence over the situation where loads and loads of data are to be collected for several hours in order to understand and identify abnormalities. Thus, HRV can be seen as highly effective diagnostic tool. In recent years, there has been much work by various researchers on the analysis of heart rate variability [10], [11], [12]. HRV also gives information about the sympathetic–parasympathetic autonomic balance and about the risk of sudden cardiac death in these patients [13]. HRV measurements are easy to obtain and they are reproducible, if measured under standardized conditions [14], [15].

Biological time series analysis can be done in time and frequency domain as well as with non-linear methods. The aim of the analysis is to detect important dynamical properties of the physiological phenomena which are hidden in the data. A particular problem of biological time series analysis comes from the fact that statistical characteristics can vary with time. This makes time domain analysis unreliable. Frequency domain parameters give better assessment of the autonomic function, but the reliability of spectral power diminishes with the decrease in power signal and signal-to-noise ratio [16].

Non-linear dynamical techniques are used in many areas including biology and medicine, because they can overcome the shortcomings of time and frequency domain methods [17]. These techniques yield useful indicators of pathologies, because many biological systems, such as the cardiovascular system, are complex and can never be linear in nature. Schumacher [18] have explained the use of linear and non-linear techniques in the analysis of HR signals. Furthermore, non-linear methods have been applied in tracking HRV signals and predicting the onset of dangerous cardiovascular related events, such as Ventricular Tachycardia and congestive heart failure [19].

The decreased beat-to-beat variability during deep breathing of patients with diabetic neuropathy was first reported by Wheeler and Watkins [20] and confirmed by many others. Studies which compared cardiac autonomic function tests and HRV indices (based on both short (5-min) and 24-h electrocardiogram (ECG) recordings), showed that in diabetic patients without abnormal function tests HRV was lowered [21]. It was concluded that cardiac (parasympathetic) autonomic activity was diminished in diabetic patients before clinical symptoms of neuropathy became evident [22], [23].

In this paper we analyze HRV signals with both linear and non-linear methods. The non-linear methods uncover subtle changes in the beat to beat variation of the signal. This makes them more robust against systematic (linear) measurement errors. Furthermore, these methods aim to reveal the underlying physiological processes directly and not, as in linear methods, measure secondary or indirect signal phenomena. Therefore, we promote non-linear methods to determine the cardiac health of diabetic patients. These measures can be used for treatment monitoring and to assess the results of clinical trials.

Section snippets

Data

The electrocardiograms (ECG) of 15 patients (10 male and 5 female) with diabetes and of 15 healthy volunteers (8 male and 7 female)) were recorded, with the patients in a relaxed supine position for 60 min. The subjects under study of diabetes were in the age group of 50 ± 70 years (mean ± standard deviation = 58.5 ± 6.42 years) and the duration of diabetes for the patient groups was 5 ± 15 years. The normal subjects were in the age group of 40 ± 60 years (mean ± standard deviation = 50 ± 8.8 years). ECG signals

Time domain

Table 1 shows t-test analysis [46] for the time domain parameters. All features are statistically significant as they possess p-values of less than 0.0001. Even though time domain parameters, such as HR¯, NN50 and PNN50, are widely used, it can be shown that results can be affected by disturbances like artifacts and noise. To effectively measure these parameters, sufficient measures have to be taken to eliminate them prior to data collection. This careful measurement is reflected in the low p

Discussion

We have shown the results of time domain, frequency domain, and non-linear analysis in the previous section. We have used reduced p-values to analyze both nonlinear and linear feature extraction methods. From past experience we know that features with a low p-value are more useful when it comes to differentiating normal and type 2 diabetic individuals. However, strictly speaking, a lower p-value just indicates that the relationship between the groups in not linear. Therefore, all we can claim

Conclusion

Diabetes, a metabolic disorder, is one of the most dangerous diseases, even though, like HIV/AIDS, not a direct killer, it seriously shortens life. HRV measurements are used as base signals to assess the cardiac health of diabetic and normal subjects.

In this report, we have extracted six non-linear features from heart rate signals from both normal and diabetes patients. The features are as follows: SD2, REC, DET, ApEn, SampEn and CD. Both ApEn and SampEn show lower entropy values for the

Acknowledgements

Authors thank Ms Loh Khei Ying for helping in running the non-linear codes on normal and diabetes heart rate signals.

References (56)

  • M. Lishner et al.

    Spectral analysis of heart rate fluctuations. A non-invasive, sensitive method for the early diagnosis of autonomic neuropathy in diabetes mellitus

    Journal of the Autonomic Nervous System

    (1987)
  • U.R. Acharya et al.

    Automatic identification of cardiac health using modeling techniques: a comparative study

    Information Science

    (2008)
  • G. Georgoulas et al.

    Novel approach for fetal heart rate classification introducing grammatical evolution

    Biomedical Signal Processing and Control

    (2007)
  • S. ArIca et al.

    Predictability of baroreflex sensitivity induced by phenylephrine injection via frequency domain indices computed from heart rate and systolic blood pressure signals during deep breathing

    Biomedical Signal Processing and Control

    (2010)
  • O. Faust et al.

    Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review

    Journal of Medical Systems

    (2010)
  • D.J. Ewing et al.

    Diagnosis and management of diabetic autonomic neuropathy

    British Medical Journal

    (1982)
  • R.A. Guthrie

    The Diabetes Sourcebook

    (1999)
  • S.M. Grundy et al.

    Diabetes and cardiovascular disease: a statement for healthcare professionals from the American Heart Association

    Circulation

    (1999)
  • Fact sheet No. 312, World health organization, http://www.who.int/mediacentre/factsheets/fs312/en/index.html (June...
  • Data from the 2007 National Diabetes Fact Sheet, American Diabetes Association,...
  • Fact sheet No. 317, World health organization, http://www.who.int/mediacentre/factsheets/fs312/en/index.html (June...
  • U.R. Acharya et al.

    Comprehensive analysis of cardiac health using heart rate signals

    Physiological Measurement

    (2004)
  • M. Malik et al.

    Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques

    European Heart Journal

    (1989)
  • Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology

    Heart rate variability: standards of measurement, physiological interpretation, and clinical use

    Circulation

    (1996)
  • G. Bernston et al.

    Heart rate variability: origins, methods, and interpretive caveats

    Psychophysiology

    (1997)
  • F. Lombardi

    Chaos theory, heart rate variability, and arrhythmic mortality

    Circulation

    (2000)
  • D. Ge et al.

    Cardiac arrhythmia classification using autoregressive modeling

    BioMedical Engineering OnLine

    (2002)
  • U.R. Acharya et al.

    Heart rate variability: a review

    Medical and Biological Engineering and Computing

    (2006)
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