Investigation of goodness of model data fit using PLSR and PCR regression models to determine informative wavelength band in NIR region for non-invasive blood glucose prediction

2019 ◽  
Vol 51 (8) ◽  
Author(s):  
S. Vasanthadev Suryakala ◽  
Shanthi Prince
Keyword(s):  
Blood Glucose ◽  
Regression Models ◽  
Wavelength Band ◽  
Model Data ◽  
Non Invasive ◽  
Glucose Prediction

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

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7815
Author(s):  
Justin Chu ◽  
Wen-Tse Yang ◽  
Wei-Ru Lu ◽  
Yao-Ting Chang ◽  
Tung-Han Hsieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Non Invasive ◽  
Physiological Diversity ◽  
Promising Solution ◽  
Glucose Prediction ◽  
Error Grid ◽  
Larger Sample

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

Portable non-invasive blood glucose apparatus based on DSP

2009 ◽  
Vol 2009 (6) ◽  
pp. 108-112 ◽  
Author(s):  
Jianming Zhu ◽  
Zhencheng Chen ◽  
Xingliang Jin ◽  
Diya Wang
Keyword(s):  
Blood Glucose ◽  
Non Invasive

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

2021 ◽  
pp. 193229682110182
Author(s):  
Aaron P. Tucker ◽  
Arthur G. Erdman ◽  
Pamela J. Schreiner ◽  
Sisi Ma ◽  
Lisa S. Chow
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Patient Specific ◽  
Sensor Location ◽  
Effective Care ◽  
Freestyle Libre ◽  
Patients With Diabetes ◽  
Interstitial Glucose ◽  
Continuous Glucose Monitors ◽  
Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

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