Non-invasive Hyperglycemia Detection via Electrocardiogram Using Discrete Wavelet Transform and Machine Learning
Classification results showed an accuracy of 97% for normal glucose levels and 93% for hyperglycemia detection using both SVM and KNN. These findings indicate that ECG signal characteristics can serve as an adjunct for non-invasive hyperglycemia detection.
Authors
- Oscar Iván Coronado-Reyes
- Luis Alfredo Castro-Pimentel (ORCID: https://orcid.org/0000-0003-0276-3047)
- José Antonio Gutiérrez Gnecchi (ORCID: https://orcid.org/0000-0001-7898-604X)
- Adriana C. Téllez-Anguiano
Institutions
- Instituto Tecnológico de Morelia (MX)
Publication Details
- Journal
- Cureus
- Published
- 2025-03-13
- DOI
- https://doi.org/10.7759/cureus.80548
- Citations
- 2
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.78