Title: Identification of type-2 diabetes by electrocardiogram signal using flexible analytical wavelet transform

Authors: Bhanupriya Mishra; Neelamshobha Nirala

Addresses: Department of Biomedical Engineering, National Institute of Technology Raipur, India ' Department of Biomedical Engineering, National Institute of Technology Raipur, India

Abstract: Type-2 diabetes mellitus (T2DM) is a lifelong metabolic disease with worldwide prevalence. It can drastically decrease the life expectancy of any subject with a huge economic burden. The present study aimed to create a non-invasive and economical tool for automatic detection of T2DM using electrocardiogram (ECG) signals. The flexible analytic wavelet transform is used to evaluate the ECG by decomposing it into predictable sub-bands. Statistical and time-domain features were extracted from each sub-band. Different feature selection techniques were applied to obtain the most relevant features. The top nine features, selected by using the one-R attribute eval feature selection technique, were fed into the various types of machine learning classifiers. In tested classifiers, the fine k-nearest neighbour and optimisable KNN classifiers have shown the highest average accuracy of 94.94% and 94.61% respectively. The results suggest that the proposed approach provides an efficient non-invasive T2DM detection method in regular applications.

Keywords: electrocardiogram signal; flexible analytical wavelet transform; FAWT; type-2 diabetes; feature extraction; feature selection methods; machine learning techniques.

DOI: 10.1504/IJBET.2023.134600

International Journal of Biomedical Engineering and Technology, 2023 Vol.43 No.3, pp.233 - 258

Received: 30 Mar 2022
Accepted: 21 Nov 2022
Published online: 30 Oct 2023 *

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