A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extracted the corrected QT interval and five heart rate variability parameters from the ECG, along with glucose level from a continuous glucose monitoring system (CGMS). This feature set is used as input to the SVM, and hypoglycemic events are predicted every 5 min using the trained SVM model for up to 30 min in advance. The proposed algorithm was developed and evaluated for nine Type-1 diabetes patients in the D1NAMO dataset. The prediction sensitivity, specificity, and accuracy values for the test set were 91.1%, 87.0%, and 89.0% (10 min before); 88.0%, 84.3%, and 86.2% (20 min before); 80.1%, 83.3%, and 81.7% (30 min before), respectively. These results show higher performance of the proposed method compared to previous studies and suggest the possibility of predicting hypoglycemia in advance.