Heart rate variability features from nonlinear cardiac dynamics in identification of diabetes using artificial neural network and support vector machine

• Autorzy: Aggarwal Yogender , Das Joyani , Mazumder Papiya Mitra , Kumar Rohit , Sinha Rakesh Kumar

Identyfikatory

DOI

10.1016/j.bbe.2020.05.001

• Języki publikacji: EN

Abstrakty

EN

Diabetes mellitus (DM) is a multifactorial disease characterized by hyperglycemia. The type 1 and type 2 DM are two different conditions with insulin deficiency and insulin resistance, respectively. It may cause atherosclerosis, stroke, myocardial infarction and other relevant complications. It also features neurological degeneration with autonomic dysfunction to meet metabolic demand. The autonomic balance controls the physiological variables that exhibit nonlinear dynamics. Thus, in current work, nonlinear heart rate variability (HRV) parameters in prognosis of diabetes using artificial neural network (ANN) and support vector machine (SVM) have been demonstrated. The digital lead-I electrocardiogram (ECG) was recorded from male Wister rats of 10–12 week of age and 200 ± 20 gm of weight from control (n = 5) as well as from Streptozotocin induced diabetic rats (n = 5). A total of 526 datasets were computed from the recorded ECG data for evaluating thirteen nonlinear HRV parameters and used for training and testing of ANN. Using these parameters as inputs, the classification accuracy of 86.3% was obtained with an ANN architecture (13:7:1) at learning rate of 0.01. While relatively better accuracy of 90.5% was observed with SVM to differentiate the diabetic and control subjects. The obtained results suggested that nonlinear HRV parameters show distinct changes due to diabetes and hence along with machine learning tools, these can be used for development of noninvasive low-cost real-time prognostic system in predicting diabetes using machine learning techniques.

Słowa kluczowe

EN

artificial neural network; diabetes mellitus; electrocardiogram; heart rate variability; support vector machine

PL

sztuczna sieć neuronowa; cukrzyca; elektrokardiogram; zmienność rytmu serca; maszyna wektorów wsparcia

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1002--1009
• Opis fizyczny: Bibliogr. 65 poz., tab.

Twórcy

autor

Aggarwal Yogender

• Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

autor

Das Joyani

• Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

autor

Mazumder Papiya Mitra

• Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

autor

Kumar Rohit

• Department of Mathematics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

autor

Sinha Rakesh Kumar

• Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).