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. 2024 Nov 7;14(22):2489.
doi: 10.3390/diagnostics14222489.

Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram

Ho-Jung Song  1 , Ju-Hyuck Han  1 , Sung-Pil Cho  2 , Sung-Il Im  3 , Yong-Suk Kim  4 , Jong-Uk Park  4
Affiliations

Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram

Ho-Jung Song et al. Diagnostics (Basel). .

Abstract

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.

Keywords: artificial intelligence model; blood glucose; dysglycemia; electrocardiogram; heart rate variability; non-invasive blood glucose monitoring.

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Conflict of interest statement

Author Sung-Pil Cho was employed by the company MEZOO Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Patient data collection process.
Figure 2
Figure 2
The architecture of the dysglycemia prediction model.
See this image and copyright information in PMC

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