Cover Image

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy,...

Full description

Saved in:
Bibliographic Details
Main Authors: Acharya, U.R., Sudarshan, V.K., Ghista, D.N., Lim, W.J.E., Molinari, F., Sankaranarayanan, M.
Format: Article
Language:English
Published: 2015
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/15756/1/Computer-aided_diagnosis_of_diabetic_subjects_by_heart_rate_variability_signals_using.pdf
http://eprints.um.edu.my/15756/
http://www.sciencedirect.com/science/article/pii/S0950705115000374
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.eprints.15756
record_format eprints
spelling my.um.eprints.157562016-04-14T06:44:29Z http://eprints.um.edu.my/15756/ Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method Acharya, U.R. Sudarshan, V.K. Ghista, D.N. Lim, W.J.E. Molinari, F. Sankaranarayanan, M. T Technology (General) TA Engineering (General). Civil engineering (General) Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy. The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02, sensitivity of 92.59 and specificity of 91.46, by using DT classifier with ten-fold cross validation. (C) 2015 Elsevier B.V. All rights reserved. 2015-06 Article PeerReviewed application/pdf en http://eprints.um.edu.my/15756/1/Computer-aided_diagnosis_of_diabetic_subjects_by_heart_rate_variability_signals_using.pdf Acharya, U.R. and Sudarshan, V.K. and Ghista, D.N. and Lim, W.J.E. and Molinari, F. and Sankaranarayanan, M. (2015) Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowledge-Based Systems, 81. pp. 56-64. http://www.sciencedirect.com/science/article/pii/S0950705115000374 10.1016/j.knosys.2015.02.005
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Acharya, U.R.
Sudarshan, V.K.
Ghista, D.N.
Lim, W.J.E.
Molinari, F.
Sankaranarayanan, M.
Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
description Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy. The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02, sensitivity of 92.59 and specificity of 91.46, by using DT classifier with ten-fold cross validation. (C) 2015 Elsevier B.V. All rights reserved.
format Article
author Acharya, U.R.
Sudarshan, V.K.
Ghista, D.N.
Lim, W.J.E.
Molinari, F.
Sankaranarayanan, M.
author_facet Acharya, U.R.
Sudarshan, V.K.
Ghista, D.N.
Lim, W.J.E.
Molinari, F.
Sankaranarayanan, M.
author_sort Acharya, U.R.
title Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
title_short Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
title_full Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
title_fullStr Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
title_full_unstemmed Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
title_sort computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
publishDate 2015
url http://eprints.um.edu.my/15756/1/Computer-aided_diagnosis_of_diabetic_subjects_by_heart_rate_variability_signals_using.pdf
http://eprints.um.edu.my/15756/
http://www.sciencedirect.com/science/article/pii/S0950705115000374
_version_ 1643690128371089408
score 13.211869

Similar Items