Key result
ECG-based machine learning detects hyperglycemia with ~93% accuracy.
Why the study?
Blood glucose levels are traditionally monitored using invasive methods, prompting the need for non-invasive approaches to detect hyperglycemia.
Does electrocardiogram (ECG) signal analysis using machine learning accurately detect hyperglycemia in adults?
Cross-Sectional (n=210)
No
Does electrocardiogram (ECG) signal analysis using machine learning accurately detect hyperglycemia in adults?
ECG-derived heart rate and heart rate variability analyzed via machine learning can accurately detect hyperglycemia, offering a potential non-invasive alternative for blood glucose monitoring.
Answers are grounded in this paper and cite their sources.
May aid non-invasive hyperglycemia detection; leaves open need for validation beyond cross-sectional data.
INTRODUCTION AND AIM: Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood glucose levels (BGLs), traditionally monitored through invasive methods. Continuous monitoring of BGL is essential to prevent severe complications. This study proposes a non-invasive approach to detect hyperglycemia by analyzing electrocardiogram (ECG) signals using the discrete wavelet transform (DWT) and machine learning techniques. The relationship between diabetes and electrocardiographic (ECG) signals is highly relevant in biomedicine, as DM is a significant risk factor for cardiovascular diseases. MATERIALS AND METHODS: A total of 210 individuals (healthy and diabetic, aged 18-70 years) were analyzed, with ECG signals processed using DWT to extract heart rate (HR) in beats per minute (bpm) and heart rate variability (HRV) parameters. These features were used as inputs for support vector machine (SVM) and k-nearest neighbors (KNN) classifiers to distinguish between normal and hyperglycemic states (>150 mg/dL). RESULTS: The DWT-based automatic feature detection achieved an accuracy of 99.8% for both HR and HRV. Pearson correlation analysis revealed moderate correlations between glucose levels and HR (0.2985) and HRV (-0.373), with a combined correlation index of 0.6428. CONCLUSION: 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.
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Coronado-Reyes et al. (2025) conducted a cross-sectional in Hyperglycemia (Diabetes Mellitus) (n=210). Electrocardiogram (ECG) feature extraction (HR and HRV) via discrete wavelet transform and machine learning classification vs. Standard invasive blood glucose measurement was evaluated on Accuracy of hyperglycemia detection (>150 mg/dL) using SVM and KNN classifiers. Electrocardiogram signal analysis using discrete wavelet transform and machine learning classifiers achieved 93% accuracy for detecting hyperglycemia and 97% accuracy for normal glucose levels.
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