Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO 2 oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment.
| Published in | Biomedical Statistics and Informatics (Volume 6, Issue 1) |
| DOI | 10.11648/j.bsi.20210601.12 |
| Page(s) | 6-13 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
DM Risk Prediction, PPG, Heart Rate, HRV, Waist Circumference, Rapid Screening, Quick Screen
| [1] | Lakka H-M, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002; 288 (21): 2709–16. |
| [2] | Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999; 22 (9): 1462–70. |
| [3] | World Health Organization. Use of glycated haemoglobin (HbA1c) in diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. World Health Organization; 2011. |
| [4] | Jones AG, Hattersley AT. The clinical utility of C-peptide measurement in the care of patients with diabetes. Diabet Med. 2013; 30 (7): 803–17. |
| [5] | Yu W, Liu T, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak. 2010; 10 (1): 16. |
| [6] | Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care. 2008; 31 (5): 1040–5. |
| [7] | Barakat NH, Bradley AP, Barakat MNH. Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed. 2010; 14 (4): 1114-20. |
| [8] | Carey VJ, Walters EE, Colditz GA, Solomon CG, Willett WC, Rosner BA, et al. Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. The Nurses’ Health Study. Am J Epidemiol. 1997; 145 (7): 614–9. |
| [9] | Alian AA, Shelley KH. Photoplethysmography. Best Pract Res Clin Anaesthesiol. 2014; 28 (4): 395–406. |
| [10] | Benichou T, Pereira B, Mermillod M, Tauveron I, Pfabigan D, Maqdasy S, et al. Heart rate variability in type 2 diabetes mellitus: A systematic review and meta–analysis. PLoS One. 2018; 13 (4): e0195166. |
| [11] | Paneni F, Beckman JA, Creager MA, Cosentino F. Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part I. Eur Heart J. 2013; 34 (31): 2436–43. |
| [12] | Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017; 5: 258. |
| [13] | Pinheiro N, Couceiro R, Henriques J, Muehlsteff J, Quintal I, Goncalves L, et al. Can PPG be used for HRV analysis? Annu Int Conf IEEE Eng Med Biol Soc. 2016; 2016: 2945–9. |
| [14] | Jan H-Y, Chen M-F, Fu T-C, Lin W-C, Tsai C-L, Lin K-P. Evaluation of coherence between ECG and PPG derived parameters on heart rate variability and respiration in healthy volunteers with/without controlled breathing. J Med Biol Eng. 2019; 39 (5): 783–95. |
| [15] | Pecchia L, Castaldo R, Montesinos L, Melillo P. Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations. Healthc Technol Lett. 2018; 5 (3): 94–100. |
| [16] | Rubins U, Grabovskis A, Grube J, Kukulis I. Photoplethysmography analysis of artery properties in patients with cardiovascular diseases. In: IFMBE Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008. p. 319–22. |
| [17] | Yousef Q, Reaz MBI, Ali MAM. The analysis of PPG morphology: Investigating the effects of aging on arterial compliance. Meas Sci Rev [Internet]. 2012; 12 (6). Available from: http://dx.doi.org/10.2478/v10048-012-0036-3. |
| [18] | Keikhosravi A, Aghajani H, Zahedi E. Discrimination of bilateral finger photoplethysmogram responses to reactive hyperemia in diabetic and healthy subjects using a differential vascular model framework. Physiol Meas. 2013; 34 (5): 513–25. |
| [19] | Reddy VR, Choudhury AD, Deshpande P, Jayaraman S, Thokala NK, Kaliaperumal V. DMSense: A non-invasive Diabetes Mellitus Classification System using Photoplethysmogram signal. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE; 2017. |
| [20] | Nirala N, Periyasamy R, Singh BK, Kumar A. Detection of type-2 diabetes using characteristics of toe photoplethysmogram by applying support vector machine. Biocybern Biomed Eng. 2019; 39 (1): 38–51. |
| [21] | Avram R, Tison G, Kuhar P, Marcus G, Pletcher M, Olgin JE, et al. Predicting diabetes from photoplethysmography using deep learning. J Am Coll Cardiol. 2019; 73 (9): 16. |
APA Style
Justin Chu, Wen-Tse Yang, Tung-Han Hsieh, Fu-Liang Yang. (2021). One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate. Biomedical Statistics and Informatics, 6(1), 6-13. https://doi.org/10.11648/j.bsi.20210601.12
ACS Style
Justin Chu; Wen-Tse Yang; Tung-Han Hsieh; Fu-Liang Yang. One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate. Biomed. Stat. Inform. 2021, 6(1), 6-13. doi: 10.11648/j.bsi.20210601.12
AMA Style
Justin Chu, Wen-Tse Yang, Tung-Han Hsieh, Fu-Liang Yang. One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate. Biomed Stat Inform. 2021;6(1):6-13. doi: 10.11648/j.bsi.20210601.12
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Download@article{10.11648/j.bsi.20210601.12,
author = {Justin Chu and Wen-Tse Yang and Tung-Han Hsieh and Fu-Liang Yang},
title = {One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate},
journal = {Biomedical Statistics and Informatics},
volume = {6},
number = {1},
pages = {6-13},
doi = {10.11648/j.bsi.20210601.12},
url = {https://doi.org/10.11648/j.bsi.20210601.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210601.12},
abstract = {Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO 2 oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment.},
year = {2021}
}
TY - JOUR T1 - One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate AU - Justin Chu AU - Wen-Tse Yang AU - Tung-Han Hsieh AU - Fu-Liang Yang Y1 - 2021/02/23 PY - 2021 N1 - https://doi.org/10.11648/j.bsi.20210601.12 DO - 10.11648/j.bsi.20210601.12 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 6 EP - 13 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20210601.12 AB - Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO 2 oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment. VL - 6 IS - 1 ER -
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