Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98652
Title: An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes
Authors: Janarthanan, Nittiagandhi
Tamura, Toshiyo
Acharya, U. Rajendra
Faust, Oliver
Sree, Subbhuraam Vinitha
Ghista, Dhanjoo N.
Dua, Sumeet
Joseph, Paul
Ahamed, V. I. Thajudin
Keywords: DRNTU::Science::Medicine::Biomedical engineering
Issue Date: 2013
Source: 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.
Series/Report no.: Computer methods in biomechanics and biomedical engineering
Abstract: 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.
URI: https://hdl.handle.net/10356/98652
http://hdl.handle.net/10220/17841
DOI: 10.1080/10255842.2011.616945
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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