An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes
Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained...
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sg-ntu-dr.10356-986522020-03-07T13:19:26Z An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes Janarthanan, Nittiagandhi Tamura, Toshiyo Acharya, U. Rajendra Faust, Oliver Sree, Subbhuraam Vinitha Ghista, Dhanjoo N. Dua, Sumeet Joseph, Paul Ahamed, V. I. Thajudin School of Mechanical and Aerospace Engineering DRNTU::Science::Medicine::Biomedical engineering Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L mean) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks. 2013-11-25T07:47:31Z 2019-12-06T19:58:09Z 2013-11-25T07:47:31Z 2019-12-06T19:58:09Z 2013 2013 Journal Article Acharya, U. R., Faust, O., Sree, S. V., Ghista, D. N., Dua, S., Joseph, P., et al. (2013). An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Computer methods in biomechanics and biomedical engineering, 16(2), 222-234. https://hdl.handle.net/10356/98652 http://hdl.handle.net/10220/17841 10.1080/10255842.2011.616945 en Computer methods in biomechanics and biomedical engineering |
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DRNTU::Science::Medicine::Biomedical engineering Janarthanan, Nittiagandhi Tamura, Toshiyo Acharya, U. Rajendra Faust, Oliver Sree, Subbhuraam Vinitha Ghista, Dhanjoo N. Dua, Sumeet Joseph, Paul Ahamed, V. I. Thajudin An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
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Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L mean) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Janarthanan, Nittiagandhi Tamura, Toshiyo Acharya, U. Rajendra Faust, Oliver Sree, Subbhuraam Vinitha Ghista, Dhanjoo N. Dua, Sumeet Joseph, Paul Ahamed, V. I. Thajudin |
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Article |
author |
Janarthanan, Nittiagandhi Tamura, Toshiyo Acharya, U. Rajendra Faust, Oliver Sree, Subbhuraam Vinitha Ghista, Dhanjoo N. Dua, Sumeet Joseph, Paul Ahamed, V. I. Thajudin |
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Janarthanan, Nittiagandhi |
title |
An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
title_short |
An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
title_full |
An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
title_fullStr |
An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
title_full_unstemmed |
An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
title_sort |
integrated diabetic index using heart rate variability signal features for diagnosis of diabetes |
publishDate |
2013 |
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https://hdl.handle.net/10356/98652 http://hdl.handle.net/10220/17841 |
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1681035243221417984 |