TY - GEN
T1 - Analysis of ECG Segments for Non-Invasive Blood Glucose Monitoring
AU - Igbe, Tobore
AU - Li, Jingzhen
AU - Liu, Yuhang
AU - Li, Sinan
AU - Kandwal, Abhishek
AU - Nie, Zedong
AU - Lei, Wang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Continuous blood glucose (BG) monitoring is necessary to avoid the deadly health complications from diabetes mellitus. The conventional method of measuring and monitoring BG is by pricking the finger which causes pain and discomfort to patients. To tackle this issue, there are research focusing on physiological signals, such as an electrocardiogram (ECG) to create a model capable of continuous glucose measurement. However, there are ECG segments that have not been considered that have the possibility of improving the performance for non-invasive BG monitoring. In this paper, we perform an oral glucose tolerance test (OGTT) on thirteen adults while continuously recording the ECG signal. A control experiment was also performed without the consumption of glucose. We captured continuous ECG signals and extracted 9 ECG segments. Boxplot and correlation coefficient analysis was performed on the extracted segments to observe the changes for BG. The result reveals a consistent pattern among QT, ST, QTC segments from each participant. HR and RR-I segments have dominant inverse behavior with a 92% correlation with the QT segment. While PRQ and QRS segments can also be included due to 85% and 77% correlation respectively with QTC segments. Whereas R-H and P-H segments have weak results with most of their values below 50%.
AB - Continuous blood glucose (BG) monitoring is necessary to avoid the deadly health complications from diabetes mellitus. The conventional method of measuring and monitoring BG is by pricking the finger which causes pain and discomfort to patients. To tackle this issue, there are research focusing on physiological signals, such as an electrocardiogram (ECG) to create a model capable of continuous glucose measurement. However, there are ECG segments that have not been considered that have the possibility of improving the performance for non-invasive BG monitoring. In this paper, we perform an oral glucose tolerance test (OGTT) on thirteen adults while continuously recording the ECG signal. A control experiment was also performed without the consumption of glucose. We captured continuous ECG signals and extracted 9 ECG segments. Boxplot and correlation coefficient analysis was performed on the extracted segments to observe the changes for BG. The result reveals a consistent pattern among QT, ST, QTC segments from each participant. HR and RR-I segments have dominant inverse behavior with a 92% correlation with the QT segment. While PRQ and QRS segments can also be included due to 85% and 77% correlation respectively with QTC segments. Whereas R-H and P-H segments have weak results with most of their values below 50%.
KW - blood glucose
KW - diabetes mellitus
KW - ECG
KW - OGTT
KW - physiological signal
UR - http://www.scopus.com/inward/record.url?scp=85082109709&partnerID=8YFLogxK
U2 - 10.1109/HealthCom46333.2019.9009596
DO - 10.1109/HealthCom46333.2019.9009596
M3 - Conference Proceeding
AN - SCOPUS:85082109709
T3 - 2019 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2019
BT - 2019 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2019
Y2 - 14 October 2019 through 16 October 2019
ER -
