Biomedical Signal Processing and Control 7 (2012) 295–302 Contents lists available at ScienceDirect Biomedical Signal Processing and Control journa l h omepage: www.elsevier.com/locate/bspc Linear and non-linear analysis of cardiac health in diabetic subjects Oliver Faust a,∗ , U.Rajendra Acharya a , Filippo Molinari b , Subhagata Chattopadhyay c , Toshiyo Tamura d a Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore b Biolab, Department of Electronics, Politecnico di Torino, Torino, Italy c School of Computer Studies, Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur 761008, Orissa, India d Department of Medical System Engineering, Chiba University, Chiba 263-8522, Japan a r t i c l e i n f o Article history: Received 5 January 2011 Received in revised form 21 May 2011 Accepted 3 June 2011 Available online 13 July 2011 Keywords: Cardiomyopathy Non-linear methods Linear methods Correlation dimension Approximate entropy Sample entropy Recurrence plot properties Poincare plot a b s t r a c t Diabetes is a chronic disease characterized by hyperglycaemia, which leads to specific long-term com- plications: 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. © 2011 Elsevier Ltd. All rights reserved. 1. 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 (cardiomy- opathy) [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 (approx- imately 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 con- secutive heart beats [9]. HRV is a non-invasive tool to assess the ∗ Corresponding author. Tel.: +65 6460 6602. E-mail address: fol2@np.edu.sg (O. Faust). 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 [10–12]. HRV also gives informa- tion 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 analy- sis 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 dimin- ishes 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 1746-8094/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bspc.2011.06.002