90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Please use this identifier to cite or link to this item: http://ir.sinica.edu.tw/handle/201000000A/84801

Title:	90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c
Authors:	Chu, Justin Yang, Wen-Tse Lu, Wei-Ru Chang, Yao-Ting Hsieh, Tung-Han Yang, Fu-Liang
Issue Date:	2021-11
Relation:	Sensors 21(23), 7815
URI:	http://ir.sinica.edu.tw/handle/201000000A/84801
ISSN:	http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=1424-8220&DestApp=JCR&RQ=IF_CAT_BOXPLOT
URL:	http://dx.doi.org/10.3390/s21237815
Appears in Collections:	應用科學研究中心

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