Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals

Anuja Jain, Anurag Verma, Amit Kumar Verma and Varun Bajaj

Computer Methods in Biomechanics and Biomedical Engineering, 2025, vol. 28, issue 12, 1847-1856

Abstract: Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10255842.2024.2342512 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gcmbxx:v:28:y:2025:i:12:p:1847-1856

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gcmb20

DOI: 10.1080/10255842.2024.2342512

Access Statistics for this article

Computer Methods in Biomechanics and Biomedical Engineering is currently edited by Director of Biomaterials John Middleton

More articles in Computer Methods in Biomechanics and Biomedical Engineering from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().