title: Automated identification of normal and diabetes heart rate signals using nonlinear measures.
abstract: Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
authors.forename: U
authors.initials: U
authors.lastname: Rajendra Acharya
authors.forename: Oliver
authors.initials: O
authors.lastname: Faust
authors.forename: Nahrizul
authors.initials: N
authors.lastname: Adib Kadri
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
authors.forename: Wenwei
authors.initials: W
authors.lastname: Yu
mesh_terms: D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D003920:Diabetes Mellitus / Q000503:physiopathology*; D004562:Electrocardiography / Q000379:methods; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: ANN; AdaBoost; ApEn; Approximate Entropy; Artificial neural network; CAD; Computer-aided diagnosis; DFA; DM; DT; Decision tree; Detrended fluctuation analysis; Diabetes mellitus; ECG; Electrocardiogram; FIS; FSC; Fuzzy Sugeno classifier; Fuzzy inference system; HR; HRV; Heart rate; Heart rate variability; LF; LLE; LS; Largest Lyapunov exponet; Least squares; Low frequency; ML; Maximum likelihood; NDDF; Normal density discriminant function; PDF; PNN; PPV; Positive predictive value; Probabilistic neural network; Probability density function; RBF; RP; RQA; Radial basis function; Recurrence plots; Recurrence quantification analysis; SVM; Support vector machine; WHO; World Health Organization; k-NN; k-Nearest neighbor algorithm