Abstract
As two typical subtypes of depression, bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage. Accurate diagnosis can provide effective treatment for patients. In this paper, we propose a deep learning framework namely SE-1DCNN-LSTM to automatically learn the latent EEG features of the two subtypes. Firstly, a SE block was used as a channel attention module to adaptively learn the weight of each electrode. Subsequently, a 1DCNN-LSTM network was applied to learn discriminative and effective patterns of EEG for MDD and BD. The noteworthy performance of proposed method was verified in 44 MDD and 26 BD patients with 81.10% and 83.16% classification accuracy in epoch-level and subject-level respectively. An extensive investigation of ablation analysis and window size of EEG epoch were conducted. Through visual analysis of electrode weights, we found that the weights of Fp1, Fp2, O1 and O2 electrodes were slightly greater. It demonstrated that the prefrontal lobe and occipital lobe may be possibly important brain regions for MDD and BD recognition. Overall, this study shows the effectiveness of the proposed model in EEG-based automatic diagnosis for MDD and BD.
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He, H., Yu, Q., Du, Y., Victor, V., Victor, T.A., Drevets, W.C., et al.: Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. J. Affect. Disord. 190, 483–493 (2016). https://doi.org/10.1016/j.jad.2015.10.042
Hirschfeld, R., Cass A.R., Holt. D.C.L, Carlson.C.A.: Screening for bipolar disorder in patients treated for depression in a family medicine clinic. The J. American Board Family Medicine 18(4), 233–239 (2005). https://doi.org/10.3122/jabfm.18.4.233
Ghaemi, S.N., Hsu, D.J., SoldaniF, F., Goodwin, F.K.: Antidepressants in bipolar disorder: the case for caution. Bipolar Disord. 5(6), 421–433 (2015). https://doi.org/10.1046/j.1399-5618.2003.00074.x
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P., et al.: Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods Biomedicine Programs in Bio-medicine 161, 103–113 (2018). https://doi.org/10.1016/j.cmpb.2018.04.012
Ay, B., et al.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43(7), 1–12 (2019). https://doi.org/10.1007/s10916-019-1345-y
Mao, W., Zhu, J., Li, X., Zhang, X., Sun, S.: Resting state EEG based depression recognition research using deep learning method. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 329–338. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_31
Mumtaz, W., Qayyumb, A.: A deep learning framework for automatic diagnosis of unipolar depression. Int. J. Med. Informatics 132, 103983 (2019). https://doi.org/10.1016/j.ijmedinf.2019.103983
Erguzel, T., Cumhur, T., Merve, C.: A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders. Comput. Biol. Med. 64, 127–137 (2015). https://doi.org/10.1016/j.compbiomed.2015.06.021
Erguzel, T.T., Sayar, G.H., Tarhan, N.: Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput. Appl. 27(6), 1607–1616 (2015). https://doi.org/10.1007/s00521-015-1959-z
Brooks, J.O., Wang, P.W, Ketter, T.A.: Functional brain imaging studies in bipolar disorder: focus on cerebral metabolism and blood flow. In: Yatham, L.N., Wang, P.W., Ketter, T.A.: (eds.) Bipolar Disorder. pp. 200–209. Wiley Online Library (2010). https://doi.org/10.1002/9780470661277.ch15
Lashgari, E., Liang, D., Maoz, U.: Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885 (2020). https://doi.org/10.1016/j.jneumeth.2020.108885
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.H.: Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Intelligence Machine 42(8), 2011–2023 (2020). https://doi.org/10.1109/TPAMI.2019.2913372
Fazli, S., Popescu, F., Danóczy, M.: Subject-independent mental state classification in single trials. Neural Netw. 22(9), 1305–1312 (2009). https://doi.org/10.1016/j.neunet.2009.06.003
Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532–541 (2020). https://doi.org/10.1109/taffc.2018.2817622
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2016). https://doi.org/10.1088/1741-2552/aace8c
Schirrmeiste, R.T., Gemein, L., Eggensperger, K., Hutter, F., Ball, T.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017). https://doi.org/10.1002/hbm.23730
Ren, S., He, K.M., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Analysis Intelligence Machine 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Gray, J.R., Braver, T.S., Raichle, M.E.: Integration of emotion and cognition in the lateral prefrontal cortex. Proc. Natl. Acad. Sci. U.S.A. 99, 4115–4120 (2002). https://doi.org/10.1073/pnas.062381899
Hosokawa, T., Momose, T., Kasai, K.: Brain glucose metabolism difference between bipolar and unipolar mood disorders in depressed and euthymic states. Progress in Neuro-Psychopharmacology and Biological Psychiatry 33(2), 243–250 (2009). https://doi.org/10.1016/j.pnpbp.2008.11.014
Kopecek, M., Barbora, T., Peter, S., Martin, B., Martin, B.: QEEG changes during switch rom depression to hypomania/mania: A case report. Neuro endocrinology letters. 29(3), 295–302 (2008)
Li, J., Xu, C., Cao, X., Gao, Q., Wang, Y., Wang, Y.F., et al.: Abnormal activation of the occipital lobes during emotion picture processing in major depressive disorder patients. Neural Regen. Res. 8(18), 1693–1701 (2013). https://doi.org/10.3969/j.issn.1673-5374.2013.18.007
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The authors would like to express thanks to First Hospital of Shanxi Medical University for providing the experimental EEG data.
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Zhao, Z., Shen, H., Hu, D., Zhang, K. (2023). SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder. In: Ying, X. (eds) Human Brain and Artificial Intelligence. HBAI 2022. Communications in Computer and Information Science, vol 1692. Springer, Singapore. https://doi.org/10.1007/978-981-19-8222-4_6
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