J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (4): 498-504.doi: 10.1007/s12204-022-2439-0

• • 上一篇    下一篇


  • 收稿日期:2020-12-08 出版日期:2022-07-28 发布日期:2022-08-11

Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models

LU Guannan1 (卢冠男), WANG Mengling1∗ (王梦灵), FOX Tamara2, JIANG Peng3 (蒋 鹏), JIANG Fusong3 (蒋伏松)   

  1. (1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai 200237, China; 2. INTERGRIS Mental Health Center, Spencer, OK 73084, USA; 3. Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200237, China)
  • Received:2020-12-08 Online:2022-07-28 Published:2022-08-11

Abstract: This paper proposes five indicators to evaluate the effectiveness and viability for adverse glycemic events detection based on predicted blood glucose (BG) values. False negative rate (FNR) and false positive rate (FPR) are defined to evaluate whether it can detect adverse glycemic events (AGEs) based on the predicted value. The temporal overlap (TO) and time difference (TD) are proposed to evaluate whether the predicted model can capture the accurate time duration of AGEs. The sum of squared percent (SSP) measures comprehensive similarity between prediction values and true values. We examined 328 patients with type 2 diabetes, containing real continuous glucose monitoring data with 5-minute time intervals. Autoregressive integrated moving average model has lower FNR and FPR. The gated recurrent unit has better temporal behavior where the mean TO with standard deviation is calculated as 0.84±0.18, and the mean TD with standard deviation is (4.39±4.01) min. Neural models have better effects on SSP (for hypoglycemia, long-short tern memory possesses 0.149 and 0.246). These five indicators are able to evaluate whether we can detect abnormal BG levels and reveal the temporal behavior of AGEs effectively. The proposed neural predictive models have more promising application in AGE detection.

Key words: adverse glycemic events detection, glucose prediction, neural network, evaluation, indicators