Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2970-2974.DOI: 10.11772/j.issn.1001-9081.2023091371
• Frontier and comprehensive applications • Previous Articles Next Articles
Jing QIN1, Zhiguang QIN1(), Fali LI2, Yueheng PENG2
Received:
2023-10-10
Revised:
2024-01-09
Accepted:
2024-01-12
Online:
2024-01-31
Published:
2024-09-10
Contact:
Zhiguang QIN
About author:
QIN Jing, born in 1994, Ph. D. candidate. His research interests include medical image, signal processing.Supported by:
通讯作者:
秦志光
作者简介:
秦璟(1994—),男,四川成都人,博士研究生,CCF会员,主要研究方向:医学图像、信号处理基金资助:
CLC Number:
Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network[J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
秦璟, 秦志光, 李发礼, 彭悦恒. 基于概率稀疏自注意力神经网络的重性抑郁疾患诊断[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2970-2974.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091371
Layer | Filters | Size | Activation | Output |
---|---|---|---|---|
Input | (1 280,21) | |||
Embedding | 256 | 3 | (1 280,256) | |
Prob Attn | 256 | 5 | GeLU | (1 280,256) |
Dropout | Dropout=0.1 | (1 280,256) | ||
Conv 1d | 256 | 3 | (1 280,256) | |
BN 1d | 256 | (1 280,256) | ||
Activation | ELU | (1 280,256) | ||
Maxpool 1d | 256 | 3 | Stride=2 | (640,256) |
Prob Attn | 128 | 5 | GeLU | (640,128) |
Dropout | Dropout=0.1 | (640,128) | ||
Conv 1d | 128 | 3 | (640,128) | |
BN 1d | 128 | (640,128) | ||
Activation | ELU | (640,128) | ||
Maxpool 1d | 64 | 3 | Stride=2 | (320,64) |
Prob Attn | 64 | 5 | GeLU | (320,64) |
Dropout | Dropout=0.1 | (320,64) | ||
Conv 1d | 64 | 3 | (320,64) | |
BN 1d | 64 | (320,64) | ||
Activation | ELU | (320,64) | ||
Maxpool 1d | 64 | 3 | Stride=2 | (160,64) |
Flatten | (10 240,) | |||
Linear | (512,) | |||
Linear | Softmax | (2,) |
Tab. 1 Network parameters of PSANet
Layer | Filters | Size | Activation | Output |
---|---|---|---|---|
Input | (1 280,21) | |||
Embedding | 256 | 3 | (1 280,256) | |
Prob Attn | 256 | 5 | GeLU | (1 280,256) |
Dropout | Dropout=0.1 | (1 280,256) | ||
Conv 1d | 256 | 3 | (1 280,256) | |
BN 1d | 256 | (1 280,256) | ||
Activation | ELU | (1 280,256) | ||
Maxpool 1d | 256 | 3 | Stride=2 | (640,256) |
Prob Attn | 128 | 5 | GeLU | (640,128) |
Dropout | Dropout=0.1 | (640,128) | ||
Conv 1d | 128 | 3 | (640,128) | |
BN 1d | 128 | (640,128) | ||
Activation | ELU | (640,128) | ||
Maxpool 1d | 64 | 3 | Stride=2 | (320,64) |
Prob Attn | 64 | 5 | GeLU | (320,64) |
Dropout | Dropout=0.1 | (320,64) | ||
Conv 1d | 64 | 3 | (320,64) | |
BN 1d | 64 | (320,64) | ||
Activation | ELU | (320,64) | ||
Maxpool 1d | 64 | 3 | Stride=2 | (160,64) |
Flatten | (10 240,) | |||
Linear | (512,) | |||
Linear | Softmax | (2,) |
模型 | 准确度 | 精确度 | 敏感度 | 特异度 | F1-score |
---|---|---|---|---|---|
EEGNet | 84.58 | 85.59 | 94.78 | 58.43 | 89.89 |
DSNet | 89.05 | 90.83 | 94.40 | 75.07 | 92.57 |
DeprNet | 90.03 | 89.23 | 95.88 | 76.77 | 93.28 |
PSANet | 91.57 | 90.28 | 99.06 | 79.98 | 94.46 |
Tab. 2 Performance comparison among different models in classification of major depressive disorder
模型 | 准确度 | 精确度 | 敏感度 | 特异度 | F1-score |
---|---|---|---|---|---|
EEGNet | 84.58 | 85.59 | 94.78 | 58.43 | 89.89 |
DSNet | 89.05 | 90.83 | 94.40 | 75.07 | 92.57 |
DeprNet | 90.03 | 89.23 | 95.88 | 76.77 | 93.28 |
PSANet | 91.57 | 90.28 | 99.06 | 79.98 | 94.46 |
模型 | 参数量 | 模型 | 参数量 |
---|---|---|---|
PSANet | 3.74 | Transformer-encoder | 90.68 |
EEGNet | 8.13 |
Tab. 3 Comparison of parameters of different models in classification of major depressive disorder
模型 | 参数量 | 模型 | 参数量 |
---|---|---|---|
PSANet | 3.74 | Transformer-encoder | 90.68 |
EEGNet | 8.13 |
1 | KENNEDY S H. Core symptoms of major depressive disorder: relevance to diagnosis and treatment [J]. Dialogues in Clinical Neuroscience, 2008, 10(3): 271-277. |
2 | CLARK M, DiBENEDETTI D, PEREZ V. Cognitive dysfunction and work productivity in major depressive disorder [J]. Expert Review of Pharmacoeconomics & Outcomes Research, 2016, 16(4): 455-463. |
3 | BECH P, BOLWIG T G, KRAMP P, et al. The Bech-Rafaelsen mania scale and the Hamilton depression scale: evaluation of homogeneity and inter-observer reliability [J]. Acta Psychiatrica Scandinavica, 1979, 59(4): 420-430. |
4 | BUZUG T M. Computed tomography [M]// Springer Handbook of Medical Technology. Berlin: Springer, 2011: 311-342. |
5 | BIASIUCCI A, FRANCESCHIELLO B, MURRAY M M. Electroencephalography [J]. Current Biology, 2019, 29(3): R80-R85. |
6 | AL-SAEGH A, DAWWD S A, ABDUL-JABBAR J M. Deep learning for motor imagery EEG-based classification: a review [J]. Biomedical Signal Processing and Control, 2021, 63: 102172. |
7 | ZHANG J, YIN Z, CHEN P, et al. Emotion recognition using multi-modal data and machine learning techniques: a tutorial and review [J]. Information Fusion, 2020, 59: 103-126. |
8 | ZHANG X, LI J, HOU K, et al. EEG-based depression detection using convolutional neural network with demographic attention mechanism [C]// Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Piscataway: IEEE, 2020: 128-133. |
9 | WANG D, LEI C, ZHANG X, et al. Identification of depression with a semi-supervised GCN based on EEG data [C]// Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine. Piscataway: IEEE, 2021: 2338-2345. |
10 | SU Y, ZHANG Z X, CAI Q, et al. 3DMKDR: 3D multiscale kernels CNN model for depression recognition based on EEG [J]. Journal of Beijing Institute of Technology, 2023, 32(2): 230-241. |
11 | SONG X W, YAN D D, ZHAO L L, et al. LSDD-EEGNet: an efficient end-to-end framework for EEG-based depression detection[J]. Biomedical Signal Processing and Control, 2022, 75: 103612. |
12 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 6000-6010. |
13 | ZHOU H, ZHANG S, PENG J, et al. Informer: beyond efficient Transformer for long sequence time-series forecasting [C]// Proceedings of the 2021 AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2021, 35(12): 11106-11115. |
14 | LIU W, ZHANG C, WANG X, et al. Functional connectivity of major depression disorder using ongoing EEG during music perception [J]. Clinical Neurophysiology, 2020, 131(10): 2413-2422. |
15 | MUMTAZ W, ALI S S A, YASIN M A M, et al. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD) [J]. Medical & Biological Engineering & Computing, 2018, 56: 233-246. |
16 | ZHANG B, YAN G, YANG Z, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 215-229. |
17 | MUMTAZ W, XIA L, MOHD YASIN M A, et al. A wavelet-based technique to predict treatment outcome for major depressive disorder [J]. PLoS ONE, 2017, 12(2): e0171409. |
18 | LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces [J]. Journal of Neural Engineering, 2018, 15(5): 056013. |
19 | DENG X, ZHANG B, YU N, et al. Advanced TSGL-EEGNet for motor imagery EEG-based brain-computer interfaces [J]. IEEE Access, 2021, 9: 25118-25130. |
20 | HUANG W, XUE Y, HU L, et al. S-EEGNet: electroencephalogram signal classification based on a separable convolution neural network with bilinear interpolation [J]. IEEE Access, 2020, 8: 131636-131646. |
21 | TSUKAHARA A, ANZAI Y, TANAKA K, et al. A design of EEGNet‐based inference processor for pattern recognition of EEG using FPGA [J]. Electronics and Communications in Japan, 2021, 104(1): 53-64. |
22 | SEAL A, BAJPAI R, AGNIHOTRI J, et al. DeprNet: a deep convolution neural network framework for detecting depression using EEG [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2505413. |
23 | TRIVEDI M H, McGRATH P J, FAVA M, et al. Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design [J]. Journal of Psychiatric Research, 2016, 78: 11-23. |
24 | XIA M, WU Y, GUO D, et al. DSNet: EEG-based spatial convolutional neural network for detecting major depressive disorder [C]// Proceedings of the 3rd International Workshop on Human Brain and Artificial Intelligence. Cham: Springer, 2022: 50-59. |
25 | WANG B, KANG Y, HUO D, et al. Depression signal correlation identification from different EEG channels based on CNN feature extraction [J]. Psychiatry Research: Neuroimaging, 2023, 328: 111582. |
26 | LI L, WANG X, LI J, et al. An EEG-based marker of functional connectivity: detection of major depressive disorder [J]. Cognitive Neurodynamics, 2024, 18: 1671-1687. |
27 | AFZALI A, KHALEGHI A, HATEF B, et al. Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals [J/OL]. Waves in Random and Complex Media, 2023: 1-16 [2023-12-23]. . |
28 | MAHDI A F, AHMED A K. Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI [J]. Indonesian Journal of Electrical Engineering and Computer Science, 2022, 28(1): 535-544. |
29 | RAVAN M, NOROOZI A, SANCHEZ M M, et al. Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers [J]. Journal of Affective Disorders, 2024, 346: 285-298. |
30 | KSIBI A, ZAKARIAH M, MENZLI L J, et al. Electroencephalography-based depression detection using multiple machine learning techniques [J]. Diagnostics, 2023, 13(10): 1779. |
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