This paper is in the following e-collection/theme issue:

mHealth for Screening (213) New Methods (944) Wearable Devices and Sensors (888) Apps, Mobile, Wearables for Diabetes (298) Hypertension Prevention and Treatment (224) Novel Sensors and Data Acquisition Methods in Cardiology (196)

Published on in Vol 7, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11959, first published .
A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study

A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study

A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study

Authors of this article:

Yiming Hao1 Author Orcid Image ;   Feng Cheng2 Author Orcid Image ;   Minh Pham3 Author Orcid Image ;   Hayley Rein4 Author Orcid Image ;   Devashru Patel5 Author Orcid Image ;   Yuchen Fang1 Author Orcid Image ;   Yiyi Feng1 Author Orcid Image ;   Jin Yan6 Author Orcid Image ;   Xueyang Song1 Author Orcid Image ;   Haixia Yan1 Author Orcid Image ;   Yiqin Wang1 Author Orcid Image
Article Authors Cited by (29) Tweetations (2) Metrics

Journals

  1. Yarza S, Hassan L, Shtein A, Lesser D, Novack L, Katra I, Kloog I, Novack V. Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies. Atmosphere 2020;11(2):122 View
  2. Tomasova L, Misak A, Kurakova L, Grman M, Ondrias K. Characterization of Rat Cardiovascular System by Anacrotic/Dicrotic Notches in the Condition of Increase/Decrease of NO Bioavailability. International Journal of Molecular Sciences 2020;21(18):6685 View
  3. Chaki J, Thillai Ganesh S, Cidham S, Ananda Theertan S. Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. Journal of King Saud University - Computer and Information Sciences 2022;34(6):3204 View
  4. Wang Z, Zhang S, Yuen P, Chan K, Chan Y, Cheung C, Chow C, Chua K, Hu J, Hu Z, Lao B, Leung C, Li H, Zhong L, Liu X, Liu Y, Liu Z, Lun X, Mo W, Siu S, Xiong Z, Yeung W, Zhang R, Zhang X. Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study. JMIR mHealth and uHealth 2020;8(7):e16018 View
  5. Ding X, Cheng F, Morris R, Chen C, Wang Y. Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation. JMIR Medical Informatics 2020;8(6):e18134 View
  6. Ding X, Wang Y, Hao Y, Lv Y, Chen R, Yan H, Rajinikanth V. A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging. Computational and Mathematical Methods in Medicine 2021;2021:1 View
  7. Yeuk-Lan Alice L, Binghe G, Shuang C, Hoyin C, Kawai K, Wenjung L, Jiangang S. Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition. Digital Chinese Medicine 2021;4(1):1 View
  8. Zhang Q, Zhou J, Zhang B. Computational Traditional Chinese Medicine diagnosis: A literature survey. Computers in Biology and Medicine 2021;133:104358 View
  9. HASEDA Y, MASUKO M, FUJIMOTO K, KOYAMA S, ISHIZAWA H. Verification of Non-invasive Blood Glucose Measurement Based on Pulse Wave by FBG Sensor System. Transactions of the Society of Instrument and Control Engineers 2021;57(7):314 View
  10. Ouyang C, Gan Z, Zhen J, Guan Y, Zhu X, Zhou P. Inter-Patient Classification With Encoded Peripheral Pulse Series and Multi-Task Fusion CNN: Application in Type 2 Diabetes. IEEE Journal of Biomedical and Health Informatics 2021;25(8):3130 View
  11. Ushakov N, Markvart A, Liokumovich L. Singlemode-Multimode-Singlemode Fiber-Optic Interferometer Signal Demodulation Using MUSIC Algorithm and Machine Learning. Photonics 2022;9(11):879 View
  12. SUI D, ZHANG L, YANG F. Data-driven based four examinations in TCM: a survey. Digital Chinese Medicine 2022;5(4):377 View
  13. Yang J, Chen L, Cai S, Xie T, Yan H. Prediction of H-type Hypertension Based on Pulse Wave MFCC Features Using Mixed Attention Mechanism. Journal of Medical and Biological Engineering 2022;42(5):579 View
  14. Hou C, Cui Y, Xu Y, Wang Y, Hao Y, Xu Y. TCM Syndrome Recognition Model of Type 2 Diabetes Mellitus in Shanghai Based on TCM Inquiry Information. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  15. Ushakov N, Markvart A, Liokumovich L. Pulse Wave Velocity Measurement With Multiplexed Fiber Optic Fabry–Perot Interferometric Sensors. IEEE Sensors Journal 2020;20(19):11302 View
  16. Ahmed A, Aziz S, Abd-alrazaq A, Farooq F, Sheikh J. Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review. Journal of Medical Internet Research 2022;24(8):e36010 View
  17. Ahmed A, Aziz S, Qidwai U, Abd-Alrazaq A, Sheikh J. Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data. Computer Methods and Programs in Biomedicine Update 2023;3:100094 View
  18. Zhang S, Zhang X, Wu Y, Zhang H. Wrist Pulse Feature of Pneumoconiosis and its Application. IEEJ Transactions on Electrical and Electronic Engineering 2023;18(2):195 View
  19. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  20. Bi Z, Yao X, Hu X, Yuan P, Guo X, Guo Z, Wang S, Li J, Shi Y, Li J, Cui J, Xu J, Hashmi M. Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  21. Wu W, Chen R, Guo R, Yan J, Zhang C, Wang Y, Yan H, Zhang Y. A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis. Irish Journal of Medical Science (1971 -) 2023;192(6):2697 View
  22. Wu W, Zhang C, Ma X, Guo R, Yan J, Wang Y, Yan H, Zhang Y. An early screening model of pulse detection technology for hepatic steatosis. Intelligent Medicine 2023;3(4):280 View
  23. Lim J, Li J, Feng X, Feng L, Xia Y, Xiao X, Wang Y, Xu Z. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. BMC Complementary Medicine and Therapies 2023;23(1) View
  24. Misak A, Grman M, Tomasova L, Makara O, Rostakova Z, Waczulikova I, Ondrias K. Use of a rat model to characterize 35 arterial pulse wave parameters in a comparative study of isoflurane and Zoletil/xylazine anesthesia and the effect ofAcanthopanax senticosusextract. Animal Models and Experimental Medicine 2023;6(5):474 View
  25. Abushouk A, Kansara T, Abdelfattah O, Badwan O, Hariri E, Chaudhury P, Kapadia S. The Dicrotic Notch: Mechanisms, Characteristics, and Clinical Correlations. Current Cardiology Reports 2023;25(8):807 View
  26. Wang M, Hou L, Xiao Y, Liu R, Han L, Nikolai M, Zhang S, Cheng C, Hu K. Highly Sensitive Flexible Sensors for Human Activity Monitoring and Personal Healthcare. Langmuir 2023;39(45):15911 View
  27. Chen J, Geng X, Yao F, Liao X, Zhang Y, Wang Y. Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network. Electronics 2024;13(3):511 View
  28. Modak S, Jha V. Machine and deep learning techniques for the prediction of diabetics: a review. Multimedia Tools and Applications 2024 View

Books/Policy Documents

  1. E. S. Handbook of Research on AI Methods and Applications in Computer Engineering. View