Current Result Document :
| 한글제목(Korean Title) |
저혈당 모니터링을 위한 AIoMT 기기 개발 |
| 영문제목(English Title) |
Development of AIoMT Device for Hypoglycemia Monitoring |
| 저자(Author) |
최준원
정하림
김도용
김한성
Jun Won Choi
Halim Chung
Do Yong Kim
Han Sung Kim
김재준
신방호
임소영
배지원
강준형
엄다현
에르덴바야르
이경중
박종욱
Jaejun Kim
Bangho Shin
Soyoung Im
Jiwon Bae
Junhyeong Kang
Dahyun Eum
Erdenebayar Urtnasan
Kyoung-Joung Lee
Jong-Uk Park
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| 원문수록처(Citation) |
VOL 45 NO. 01 PP. 1818 ~ 1819 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A AIoMT Device for prediction of 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 minusing 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.
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| 키워드(Keyword) |
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