A method of mental disorder recognition based on visibility graph_Journal of Biomedical Engineering

Authors：

 ZHANG Bingtao ^1,2,3 , WEI Dan ¹ , CHANG Wenwen ¹ , YANG Zhifei ¹ , LI Yanlin ^3,4

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;
2. Key Laboratory of Opto-technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;
3. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, P. R. China;
4. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, P. R. China;

Corresponding author：

ZHANG Bingtao, Email: zhangbt14@lzu.edu.cn

Keywords：

Visibility graph; Information complementarity; Electroencephalograms; Mental disorder recognition

DOI：

10.7507/1001-5515.202208077

Video：

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Abstract Full text Figures/Tables Video References Cited by

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.

Citation： ZHANG Bingtao, WEI Dan, CHANG Wenwen, YANG Zhifei, LI Yanlin. A method of mental disorder recognition based on visibility graph. Journal of Biomedical Engineering, 2023, 40(3): 442-449. doi: 10.7507/1001-5515.202208077 Copy

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2.	Herrman H, Patel V, Kieling C, et al. Time for united action on depression: a Lancet-World psychiatric association commission. Lancet, 2022, 399(10328): 957-1022.
3.	Zhang Xiaowei, Hu Bin, Ma Xu, et al. Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans Nanobioscience, 2015, 14(2): 237-247.
4.	Li X, Hu B, Sun S, et al. EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed, 2016, 136: 151-161.
5.	梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
6.	张冰涛, 周文颖, 李延林, 等. 基于脑功能网络的抑郁症识别研究. 生物医学工程学杂志, 2022, 39(1): 47-55.
7.	Peng H, Li C, Chao J, et al. A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation. Neurocomputing, 2021, 424: 179-192.
8.	Zhang B, Yan G, Yang Z, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 215-229.
9.	Zhang X, Liu J, Shen J, et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine. IEEE Trans Cybern, 2021, 51(9): 4386-4399.
10.	Bruder G E, Bansal R, Tenke C E, et al. Relationship of resting EEG with anatomical MRI measures in individuals at high and low risk for depression. Hum Brain Mapp, 2012, 33(6): 1325-1333.
11.	马江河, 孙颖, 张雪英. 融合语音信号和脑电信号的多模态情感识别. 西安电子科技大学学报, 2019, 46(1): 143-150.
12.	Zhang Xiaowei, Shen Jian, Din Z U, et al. Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE J Biomed Health Inform, 2019, 23(6): 2265-2275.
13.	杨晓莉, 蔺素珍. 一种注意力机制的多波段图像特征级融合方法. 西安电子科技大学学报, 2020, 47(1): 120-127.
14.	Zhang B, Cai H, Song Y, et al. Computer-aided recognition based on decision-level multimodal fusion for depression. IEEE J Biomed Health Inform, 2022, 26(7): 3466-3477.
15.	Zhang X W, Pan J, Shen J, et al. Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection. IEEE Trans Affect Comput, 2022, 13(2): 958-971.
16.	Liang S, Kuo C, Hu Y, et al. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. IEEE T Instrum Meas, 2012, 61(6): 1649-1657.
17.	He Y, Wei C, Long H, et al. Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data. Applied Soft Computing, 2018, 70: 959-979.
18.	Zhang B, Cao P. Classification of high dimensional biomedical data based on feature selection using redundant removal. Plos One. 2019, 14(4): e0214406.
19.	Cai H S, Zhang X Z, Zhang Y H, et al. A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affect Comput, 2020, 11(3): 383-392.
20.	Blinowska K J, Rakowski F, Kaminski M, et al. Functional and effective brain connectivity for discrimination between Alzheimer's patients and healthy individuals: a study on resting state EEG rhythms. Clinical Neurophysiology, 2017, 128(4): 667-680.
21.	Cai H, Yuan Z, Gao Y, et al. A multi-modal open dataset for mental-disorder analysis. Sci Data, 2022, 9(1): 178.
22.	Song Z, Deng B, Wang J, et al. Biomarkers for Alzheimer's disease defined by a novel brain functional network measure. IEEE Trans Biomed Eng, 2019, 66(1): 41-49.
23.	Peng H, Hu B, Shi Q, et al. Removal of ocular artifacts in EEG an improved approach combining DWT and ANC for portable applications. IEEE J Biomed Health Inform, 2013, 17(3): 600-607.
24.	Shen J, Zhang Y, Liang H, et al. Exploring the intrinsic features of EEG signals via empirical mode decomposition for depression recognition. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 356-365.

1. Hou F, Bi F, Jiao R, et al. Gender differences of depression and anxiety among social media users during the COVID-19 outbreak in China: a cross-sectional study. BMC Public Health, 2020, 20(1): 1648.
2. Herrman H, Patel V, Kieling C, et al. Time for united action on depression: a Lancet-World psychiatric association commission. Lancet, 2022, 399(10328): 957-1022.
3. Zhang Xiaowei, Hu Bin, Ma Xu, et al. Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans Nanobioscience, 2015, 14(2): 237-247.
4. Li X, Hu B, Sun S, et al. EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed, 2016, 136: 151-161.
5. 梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
6. 张冰涛, 周文颖, 李延林, 等. 基于脑功能网络的抑郁症识别研究. 生物医学工程学杂志, 2022, 39(1): 47-55.
7. Peng H, Li C, Chao J, et al. A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation. Neurocomputing, 2021, 424: 179-192.
8. Zhang B, Yan G, Yang Z, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 215-229.
9. Zhang X, Liu J, Shen J, et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine. IEEE Trans Cybern, 2021, 51(9): 4386-4399.
10. Bruder G E, Bansal R, Tenke C E, et al. Relationship of resting EEG with anatomical MRI measures in individuals at high and low risk for depression. Hum Brain Mapp, 2012, 33(6): 1325-1333.
11. 马江河, 孙颖, 张雪英. 融合语音信号和脑电信号的多模态情感识别. 西安电子科技大学学报, 2019, 46(1): 143-150.
12. Zhang Xiaowei, Shen Jian, Din Z U, et al. Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE J Biomed Health Inform, 2019, 23(6): 2265-2275.
13. 杨晓莉, 蔺素珍. 一种注意力机制的多波段图像特征级融合方法. 西安电子科技大学学报, 2020, 47(1): 120-127.
14. Zhang B, Cai H, Song Y, et al. Computer-aided recognition based on decision-level multimodal fusion for depression. IEEE J Biomed Health Inform, 2022, 26(7): 3466-3477.
15. Zhang X W, Pan J, Shen J, et al. Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection. IEEE Trans Affect Comput, 2022, 13(2): 958-971.
16. Liang S, Kuo C, Hu Y, et al. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. IEEE T Instrum Meas, 2012, 61(6): 1649-1657.
17. He Y, Wei C, Long H, et al. Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data. Applied Soft Computing, 2018, 70: 959-979.
18. Zhang B, Cao P. Classification of high dimensional biomedical data based on feature selection using redundant removal. Plos One. 2019, 14(4): e0214406.
19. Cai H S, Zhang X Z, Zhang Y H, et al. A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affect Comput, 2020, 11(3): 383-392.
20. Blinowska K J, Rakowski F, Kaminski M, et al. Functional and effective brain connectivity for discrimination between Alzheimer's patients and healthy individuals: a study on resting state EEG rhythms. Clinical Neurophysiology, 2017, 128(4): 667-680.
21. Cai H, Yuan Z, Gao Y, et al. A multi-modal open dataset for mental-disorder analysis. Sci Data, 2022, 9(1): 178.
22. Song Z, Deng B, Wang J, et al. Biomarkers for Alzheimer's disease defined by a novel brain functional network measure. IEEE Trans Biomed Eng, 2019, 66(1): 41-49.
23. Peng H, Hu B, Shi Q, et al. Removal of ocular artifacts in EEG an improved approach combining DWT and ANC for portable applications. IEEE J Biomed Health Inform, 2013, 17(3): 600-607.
24. Shen J, Zhang Y, Liang H, et al. Exploring the intrinsic features of EEG signals via empirical mode decomposition for depression recognition. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 356-365.

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A method of mental disorder recognition based on visibility graph

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