Towards Improved Short-term Hypoglycemia Prediction and Diabetes Management based on Refined Heart Rate Data

2026-03-20

AI Summary

This study addresses the degradation in prediction accuracy for hypoglycemic events in type 1 diabetes patients caused by missing heart rate data. To mitigate this issue, the authors propose two novel imputation methods: Controlled Rational Bézier Curves (CRBC) and Cubic Hermite Interpolation with Extreme-value Control Points (CMPV). A comprehensive evaluation framework is developed that integrates root mean square error (RMSE) with the ability to preserve temporal patterns. Experimental results demonstrate that CMPV achieves a superior average composite score of 0.33, significantly outperforming existing imputation techniques. This approach effectively enhances heart rate data completeness and improves short-term hypoglycemia prediction performance, thereby offering robust support for precision diabetes management.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Data Mining & Knowledge Management: Data Compression

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Hypoglycemia is a severe condition of decreased blood glucose, specifically below 70 mg/dL (3.9 mmol/L). This condition can often be asymptomatic and challenging to predict in individuals with type 1 diabetes (T1D). Research on hypoglycemic prediction typically uses a combination of blood glucose readings and heart rate data to predict hypoglycemic events. Given that these features are collected through wearable sensors, they can sometimes have missing values, necessitating efficient imputation methods. This work makes significant contributions to the current state of the art by introducing two novel imputation techniques for imputing heart rate values over short-term horizons: Controlled Weighted Rational Bézier Curves (CRBC) and Controlled Piecewise Cubic Hermite Interpolating Polynomial with mapped peaks and valleys of Control Points (CMPV). In addition to these imputation methods, we employ two metrics to capture data patterns, alongside a combined metric that integrates the strengths of both individual metrics with RMSE scores for a comprehensive evaluation of the imputation techniques. According to our combined metric assessment, CMPV outperforms the alternatives with an average score of 0.33 across all time gaps, while CRBC follows with a score of 0.48. These findings clearly demonstrate the effectiveness of the proposed imputation methods in accurately filling in missing heart rate values. Moreover, this study facilitates the detection of abnormal physiological signals, enabling the implementation of early preventive measures for more accurate diagnosis.

Problem

• hypoglycemia prediction
• type 1 diabetes
• heart rate data
• missing data imputation
• wearable sensors

Innovation

• imputation methods
• heart rate data
• hypoglycemia prediction
• Bézier curves
• Hermite interpolation