Research on type 2 diabetes prediction algorithm based on photoplethysmography_Journal of Biomedical Engineering

Authors：

HU Mingying ,  WU Quanyu , CAO Yifan , CAO Jin , ZHAO Yifan , ZHANG Lin , LIU Xiaojie

School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, P. R. China;

Corresponding?author：

WU Quanyu, Email: wuquanyu@jsut.edu.cn

Keywords：

Photoplethysmography; Type 2 diabetes; Generative adversarial networks; Self-attention; Data augmentation

DOI：

10.7507/1001-5515.202501006

Video：

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Abstract Full text Figures/Tables Video References Cited by

To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.

1.	張杰, 丁祥龍, 龍妍, 等. 1990~2019年中國2型糖尿病發病趨勢及2020~2030年預測. 華中科技大學學報, 2024, 53(3): 315-320.
2.	楊會芳, 袁璐, 吳結鳳, 等. 基于國家基本公共衛生服務體檢的中老年人2型糖尿病風險預測模型構建. 四川大學學報, 2024, 55(3): 662-670.
3.	Ma C X, Ma X N, Guan C H, et al. Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management. Cardiovasc Diabetol, 2022, 21(1): 74.
4.	Loh H W, Xu S, Faust O, et al. Application of photoplethysmography signals for healthcare systems: An in-depth review. Comput Methods Programs Biomed, 2022, 216: 106677.
5.	Zanelli S, El Yacoubi M A, Hallab M, et al. Type 2 diabetes detection with light CNN from single raw PPG wave. IEEE Access, 2023, 11: 57652-57665.
6.	Ramasahayam S, Koppuravuri S H, Arora L, et al. Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse delayed function model of neuron. J Med Syst, 2015, 39(1): 1-15.
7.	Li G, Watanabe K, Anzai H, et al. Pulse-wave-pattern classification with a convolutional neural network. Sci Rep, 2019, 9(1): 1-11.
8.	Moreno E M, Lujan M J, Rusiol M T, et al. Type 2 diabetes screening test by means of a pulse oximeter. IEEE Trans Biomed Eng, 2017, 64(2): 341-351.
9.	Avram R, Tison G, Kuhar P, et al. Predicting diabetes from photoplethysmography using deep learning. J Am Coll Cardiol, 2019, 73(9): 16.
10.	Avram R, Olgin J E, Kuhar P, et al. A digital biomarker of diabetes from smartphone-based vascular signals. Nat Med, 2020, 26(10): 1576-1582.
11.	Ding C, Xiao R, Do D H, et al. Log-spectral matching gan: Ppg-based atrial fibrillation detection can be enhanced by gan-based data augmentation with integration of spectral loss. IEEE J Biomed Health Inform, 2023, 27(3): 1331-1341.
12.	Li J, Chen Z, Cheng L, et al. Energy data generation with wasserstein deep convolutional generative adversarial networks. Energy, 2022, 257: 124694.
13.	Sisman B, Zhang M, Sakti S, et al. Adaptive wavenet vocoder for residual compensation in gan-based voice conversion// 2018 IEEE SLT. Athens: IEEE, 2018: 282-289.
14.	Habashi A G, Azab A M, Eldawlatly S, et al. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil, 2023, 20(1): 40.
15.	Zhang A, Su L, Zhang Y, et al. EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN. Complex Intell Syst, 2021: 1-13.
16.	Tian C, Ma Y, Cammon J, et al. Dual-encoder VAE-GAN with spatiotemporal features for emotional EEG data augmentation. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2018-2027.
17.	Chang J, Hu F, Xu H, et al. Data augmentation of wrist pulse signal for traditional chinese medicine using Wasserstein GAN// Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. New York: Association for Computing Machinery, 2021: 426-430.
18.	Sohn J, Shin H, Lee J, et al. Validation of electrocardiogram based photoplethysmogram generated using U-net based generative adversarial networks. J Healthc Inform Res, 2024, 8(1): 140-157.
19.	Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014, 2: 2672-2680.
20.	Abu-Srhan A, Abushariah M A M, Al-Kadi O S. The effect of loss function on conditional generative adversarial networks. J King Saud Univ Comput Inf Sci, 2022, 34(9): 6977-6988.
21.	Huang L, Li L, Wei X, et al. Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP. Soft Comput, 2022, 26(20): 10607-10621.
22.	Ullah S, Pirahandeh M, Kim D H. Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition. Neurocomputing, 2024, 571: 127157.
23.	Liang Y, Chen Z, Liu G, et al. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Sci Data, 2018, 5(1): 1-7.

1. 張杰, 丁祥龍, 龍妍, 等. 1990~2019年中國2型糖尿病發病趨勢及2020~2030年預測. 華中科技大學學報, 2024, 53(3): 315-320.
2. 楊會芳, 袁璐, 吳結鳳, 等. 基于國家基本公共衛生服務體檢的中老年人2型糖尿病風險預測模型構建. 四川大學學報, 2024, 55(3): 662-670.
3. Ma C X, Ma X N, Guan C H, et al. Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management. Cardiovasc Diabetol, 2022, 21(1): 74.
4. Loh H W, Xu S, Faust O, et al. Application of photoplethysmography signals for healthcare systems: An in-depth review. Comput Methods Programs Biomed, 2022, 216: 106677.
5. Zanelli S, El Yacoubi M A, Hallab M, et al. Type 2 diabetes detection with light CNN from single raw PPG wave. IEEE Access, 2023, 11: 57652-57665.
6. Ramasahayam S, Koppuravuri S H, Arora L, et al. Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse delayed function model of neuron. J Med Syst, 2015, 39(1): 1-15.
7. Li G, Watanabe K, Anzai H, et al. Pulse-wave-pattern classification with a convolutional neural network. Sci Rep, 2019, 9(1): 1-11.
8. Moreno E M, Lujan M J, Rusiol M T, et al. Type 2 diabetes screening test by means of a pulse oximeter. IEEE Trans Biomed Eng, 2017, 64(2): 341-351.
9. Avram R, Tison G, Kuhar P, et al. Predicting diabetes from photoplethysmography using deep learning. J Am Coll Cardiol, 2019, 73(9): 16.
10. Avram R, Olgin J E, Kuhar P, et al. A digital biomarker of diabetes from smartphone-based vascular signals. Nat Med, 2020, 26(10): 1576-1582.
11. Ding C, Xiao R, Do D H, et al. Log-spectral matching gan: Ppg-based atrial fibrillation detection can be enhanced by gan-based data augmentation with integration of spectral loss. IEEE J Biomed Health Inform, 2023, 27(3): 1331-1341.
12. Li J, Chen Z, Cheng L, et al. Energy data generation with wasserstein deep convolutional generative adversarial networks. Energy, 2022, 257: 124694.
13. Sisman B, Zhang M, Sakti S, et al. Adaptive wavenet vocoder for residual compensation in gan-based voice conversion// 2018 IEEE SLT. Athens: IEEE, 2018: 282-289.
14. Habashi A G, Azab A M, Eldawlatly S, et al. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil, 2023, 20(1): 40.
15. Zhang A, Su L, Zhang Y, et al. EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN. Complex Intell Syst, 2021: 1-13.
16. Tian C, Ma Y, Cammon J, et al. Dual-encoder VAE-GAN with spatiotemporal features for emotional EEG data augmentation. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2018-2027.
17. Chang J, Hu F, Xu H, et al. Data augmentation of wrist pulse signal for traditional chinese medicine using Wasserstein GAN// Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. New York: Association for Computing Machinery, 2021: 426-430.
18. Sohn J, Shin H, Lee J, et al. Validation of electrocardiogram based photoplethysmogram generated using U-net based generative adversarial networks. J Healthc Inform Res, 2024, 8(1): 140-157.
19. Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014, 2: 2672-2680.
20. Abu-Srhan A, Abushariah M A M, Al-Kadi O S. The effect of loss function on conditional generative adversarial networks. J King Saud Univ Comput Inf Sci, 2022, 34(9): 6977-6988.
21. Huang L, Li L, Wei X, et al. Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP. Soft Comput, 2022, 26(20): 10607-10621.
22. Ullah S, Pirahandeh M, Kim D H. Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition. Neurocomputing, 2024, 571: 127157.
23. Liang Y, Chen Z, Liu G, et al. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Sci Data, 2018, 5(1): 1-7.

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