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ISSN 2542-1905 (Online)

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Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
004.78
DOI: 
10.18500/0869-6632-003114
EDN: 
EOIBSY

Efficiency of convolutional neural networks of different architecture for the task of depression diagnosis from EEG data

Autors: 
Shusharina Natalia Nikolaevna, Immanuel Kant Baltic Federal University
Abstract: 

The purpose of this paper is to comparatively analyse the efficiency of using artificial neural networks with different convolutional and recurrent architectures in the task of depression diagnosis based on electroencephalogram (EEG) data. Open datasets were chosen as objects of the study and own EEG data of real patients with depression were collected.

Methods. To solve the problem of identifying biomarkers of depressive disorder from EEG data, we used convolutional neural networks using two-dimensional or one-dimensional convolution operation, as well as hybrid models of convolutional and recurrent neural networks. To test the developed models of artificial neural networks, we selected open data sets, performed an experiment to collect our own data from real depressed patients, and merged the prepared data sets.

The result of this work is analysis and comparison of the performance of different classifiers based on convolutional and recurrent neural network models.

Conclusion. We show that the average accuracy of classification of depressive disorder in a test sample using cross-validation was 0.68. The results are consistent with the known results from the literature for small patient-disaggregated datasets. Although the accuracy obtained in this task is insufficient for practical application of the model, it can be argued that further research to improve the efficiency of the model is promising, as well as the need to create a sufficiently large representative dataset of depressed patients, which is an important scientific task for further construction of biophysical models of depressive disorders.

Key words: 
non-invasive electroencephalogram
depression diagnosis
deep learning
convolutional neural networks (CNN)
recurrent neural networks (RNN)
Acknowledgments: 
This work was supported by grant RSF (project No 23-71-30010)
Reference: 
  1. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nature medicine. 2022;28(1):31–38. DOI: 10.1038/s41591-021-01614-0.
  2. Abersek B, Flogie A, Pesek I. AI and Cognitive Modelling for Education. Springer Nature; 2023. 230 p. DOI: 10.1007/978-3-031-35331-4.
  3. Karpov OE, Pitsik EN, Kurkin SA, Maksimenko VA, Gusev AV, Shusharina NN, Hramov AE. Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. International Journal of Environmental Research and Public Health. 2023; 20(7):5335. DOI: 10.3390/ijerph20075335.
  4. Karpov OE, Hramov AE. Information technologies, computing systems and artificial intelligence in medicine. M.: DPK Press. 2022. 480 p (in Russian).
  5. Tondo G, De Marchi F.. From biomarkers to precision medicine in neurodegenerative diseases: Where are we? Journal of Clinical Medicine. 2022;11(15):4515. DOI: 10.3390/jcm11154515.
  6. Strafella C, Caputo V, Galota MR, Zampatti S, Marella G, Mauriello S, Cascella R, Giardina E. Application of precision medicine in neurodegenerative diseases. Frontiers in neurology. 2018;9:701. DOI: 10.3389/fneur.2018.00701.
  7. Chepurova A, Hramov A, Kurkin S. Motor Imagery: How to Assess, Improve Its Performance, and Apply It for Psychosis Diagnostics. Diagnostics. 2022;12(4):949. DOI: 10.3390/diagnostics1204094.
  8. Zhang Sh, Zhao H, Wang W, Wang Zh, Luo X, Hramov A, Kurths J. Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease. Neurocomputing. 2023;552:126512. DOI: 10.1016/j.neucom.2023.126512.
  9. Andreev AV, Kurkin SA, Stoyanov D, Badarin AA, Paunova R, Hramov AE. Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures. Chaos. 2023;33(6):063140. DOI: 10.1063/5.0155567.
  10. Sysoeva O, Maximenko V, Kuc A, Voinova V, Martynova O, Hramov A. Abnormal spectral and scale-free properties of resting-state EEG in girls with Rett syndrome. Scientific Reports. 2023;13:12932. DOI: 10.1038/s41598-023-39398-7.
  11. Law ZK, Todd C, Mehraram R, Schumacher J, Baker MR, LeBeau FE, Yarnall A, Onofrj M, Bonanni L, Thomas A, Taylor JP. The role of EEG in the diagnosis, prognosis and clinical correlations of dementia with Lewy bodies—a systematic review. Diagnostics. 2020;10(9):616. DOI: 10.3390/diagnostics10090616.
  12. Schjønning Nielsen M, Simonsen AH, Siersma V, Engedal K, Jelic V, Andersen BB, Naik M, Hasselbalch SG, Høgh P. Quantitative electroencephalography analyzed by statistical pattern recognition as a diagnostic and prognostic tool in mild cognitive impairment: results from a nordic multicenter cohort study. Dementia and Geriatric Cognitive Disorders Extra. 2019;8(3):426–438. DOI: 10.1159/000490788.
  13. Gouw AA. Clinical appications of EEG/MEG in AD: diagnosis, prognosis and treatment monitoring. Alzheimer’s & Dementia. 2023;19(S12):e073238. DOI: 10.1002/alz.073238.
  14. Torres-Simon L, Doval S, Nebreda A, Llinas SJ, Marsh EB, Maestu F. Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review. NeuroImage: Clinical. 2019;35:103040. DOI: 10.1016/j.nicl.2022.103040.
  15. Yahata N, Kasai K, Kawato M. Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry and clinical neurosciences. 2016;71(4):215–237. DOI: 10.1111/pcn.12502.
  16. Karpov OE, Hramov AE. Prognostic medicine. Physician and information technologies. 2021;3: 20–37 (in Russian). DOI: 10.25881/18110193_2021_3_20.
  17. Pitsik EN, Maximenko VA, Kurkin SA, Sergeev AP, Stoyanov D, Paunova R, Kandilarova S, Simeonova D, Hramov AE. The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder. Chaos, Solitons & Fractals. 2023;167:113041. DOI: 10.1016/j.chaos.2022.113041
  18. Boronina A, Maksimenko V, Hramov AE. Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data. Mathematics. 2023;11(11):2515. DOI: 10.3390/math11112515
  19. Mumtaz W. MDD Patients and Healthy Controls EEG Data (New). figshare. 2016. Dataset. DOI: 10.6084/m9.figshare.4244171.v2
  20. Cavanagh J. EEG: Depression rest. OpenNeuro. 2021. Dataset. DOI: 10.18112/openneuro. ds003478.v1.1.0
  21. Cai H, Yuan Z., Gao Y, Sun S, Li N, Tian F, Xiao H, Li J, Yang Z, Li X, Zhao Q, Liu Z, Yao Z, Yang M, Peng H, Zhu J, Zhang X, Gao G, Zheng F, Li R, Guo Z, Ma R, Yang J, Zhang L, Hu X, Li Y, Hu B. MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis. arXiv:2002.09283. 2020. Dataset. DOI: 10.48550/arXiv.2002.09283.
  22. MODMA Dataset. URL: http://modma.lzu.edu.cn/data/application/.
  23. Beck AT, Ward C, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Archives of general psychiatry. 1961;4(6):561–571. DOI: 10.1001/archpsyc.1961.01710120031004.
  24. Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Medical & biological engineering & computing. 2018. Vol. 56, no. 2. P. 233–246. DOI: 10.1007/s11517-017-1685-z.
  25. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Hamalainen M. MEG and EEG data analysis with MNE-Python. Frontiers in neuroscience. 2013;7:267. DOI: 10.3389/fnins.2013.00267.
  26. Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS. Electroencephalogram (EEG)- based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control. 2017;31:108–115. DOI: 10.1016/j.bspc.2016.07.006.
  27. Stephan KE, Schlagenhauf F, Huys QJ, Raman S, Aponte EA, Brodersen KH, Rigoux L, Moran RJ, Daunizeau J, Dolan RJ, Friston KJ, Heinz A. Computational neuroimaging strategies for single patient predictions. Neuroimage. 2017;145(Part B):180–199. DOI: 10.1016/j.neuroimage.2016. 06.038.
  28. de Bardeci M, Ip CT, Olbrich S. Deep learning applied to electroencephalogram data in mental disorders: A systematic review. Biological Psychology. 2021;162:108117. DOI: 10.1016/ j.biopsycho.2021.108117.
  29. Rivera MJ, Teruel MA, Mate A, Trujillo J. Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artificial Intelligence Review. 2022;55:1209–1251. DOI: 10.1007/s10462-021-09986-y.
  30. Mumtaz W, Malik AS., Ali SSA, Yasin MAM. P300 intensities and latencies for major depressive disorder detection. 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). 2015;542–545. DOI: 10.1109/ICSIPA.2015.7412250.
  31. Mumtaz W, Qayyum A. A deep learning framework for automatic diagnosis of unipolar depression. International journal of medical informatics. 2019;132:103983. DOI: 10.1016/j.ijmedinf.2019. 103983.
  32. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated EEG-based screening of depression using deep convolutional neural network. Computer methods and programs in biomedicine. 2018;161:103–113. DOI: 10.1016/j.cmpb.2018.04.012.
  33. Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, Acharya UR. Automated depression detection using deep representation and sequence learning with EEG signals. Journal of medical systems. 2019;43:205. DOI: 10.1007/s10916-019-1345-y.
  34. Sandheep P, Vineeth S, Poulose M, Subha DP. Performance analysis of deep learning CNN in classification of depression EEG signals. 2019 IEEE Region 10 Conference (TENCON). 2019;1339–1344. DOI: 10.1109/TENCON.2019.8929254.
  35. Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural engineering. 2018;15(5):056013. DOI: 10.1088/1741-2552/aace8c.
  36. Cun YL, Bottou L, Orr G, Muller K. Efficient backprop. Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg. 2012;9–48. DOI: 10.1007/978-3-642-35289-8_3.
  37. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research. 2011;12:2825–2830.
  38. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Zheng X. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467. 2016. Dataset. DOI: 10.48550/arXiv.1603.04467.
  39. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv:1412.6980. 2014. Dataset. DOI: 10.48550/arXiv.1412.6980.
  40. Hutter F, Lucke J, Schmidt-Thieme L. Beyond manual tuning of hyperparameters. KI-Kunstliche Intelligenz. 2015;29(4):329–337. DOI: 10.1007/s13218-015-0381-0.
  41. Bergstra J, Bengio Y. Random search for hyper-parameter optimization. Journal of Machine Learning Research. 2012;13:281–305.
  42. KerasTuner. URL: https://github.com/keras-team/keras-tuner.
  43. Lashgari E, Liang D, Maoz U. Data augmentation for deep-learning-based electroencephalography. Journal of Neuroscience Methods. 2020;346:108885. DOI: 10.1016/j.jneumeth.2020.108885.
  44. Koppe G., Meyer-Lindenberg A., Durstewitz D. Deep learning for small and big data in psychiatry // Neuropsychopharmacology Reviews. 2021. Vol. 46. P. 176–190. DOI: doi.org/10.1038/s41386- 020-0767-z.
  45. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Molecular Psychiatry. 2019;24:1583–1598. DOI: doi.org/10.1038/s41380-019-0365-9.
  46. Zhang Z, Lin W, Liu M, Mahmoud M.. Multimodal Deep Learning Framework for Mental Disorder Recognition. 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina. 2020;344–350. DOI: 10.1109/FG47880. 2020.00033.
Received: 
11.02.2024
Accepted: 
14.03.2024
Available online: 
02.07.2024
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