Abstract
Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows that the proposed hybrid model achieve highest accuracy, f1 score 99.66%, 99.93% and 98.87%, 99.12% for eye open and eye close dataset respectively in comparison to state of the art methods. Based on high performance, the proposed hybrid approach can be used for assessment of depression for clinical applications and can deployed remotely in hospital or private clinics for clinical evaluation.
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References
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P.: Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. 161, 103–113 (2018)
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
Huang, K.Y., Wu, C.H., Su, M.H.: Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses. Pattern Recogn. 88, 668–678 (2019)
Li, X., et al.: EEG-based mild depression recognition using convolutional neural network. Med. Biol. Eng. Comput. 57(6), 1341–1352 (2019). https://doi.org/10.1007/s11517-019-01959-2
Liao, S.C., Wu, C.T., Huang, H.C., Cheng, W.T., Liu, Y.H.: Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors 17(6), 1385 (2017)
Mahato, S., Paul, S.: Detection of major depressive disorder using linear and non-linear features from EEG signals. Microsyst. Technol. 25(3), 1065–1076 (2018). https://doi.org/10.1007/s00542-018-4075-z
Mdhaffar, A., et al.: DL4DED: deep learning for depressive episode detection on mobile devices. In: Pagán, J., Mokhtari, M., Aloulou, H., Abdulrazak, B., Cabrera, M.F. (eds.) ICOST 2019. LNCS, vol. 11862, pp. 109–121. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32785-9_10
Mumtaz, W., Qayyum, A.: A deep learning framework for automatic diagnosis of unipolar depression. Int. J. Med. Informatics 132, 103983 (2019)
Razzak, I., Blumenstein, M., Xu, G.: Multiclass support matrix machines by maximizing the inter-class margin for single trial EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 27(6), 1117–1127 (2019)
Razzak, I., Hameed, I.A., Xu, G.: Robust sparse representation and multiclass support matrix machines for the classification of motor imagery EEG signals. IEEE J. Transl. Eng. Health Med. 7, 1–8 (2019)
Razzak, M.I., Imran, M., Xu, G.: Big data analytics for preventive medicine. Neural Comput. Appl. 32(9), 4417–4451 (2019). https://doi.org/10.1007/s00521-019-04095-y
Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: overview, challenges and the future. In: Dey, N., Ashour, A.S., Borra, S. (eds.) Classification in BioApps. LNCVB, vol. 26, pp. 323–350. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65981-7_12
Yıldırım, Ö., Baloglu, U.B., Acharya, U.R.: A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl. 32(20), 15857–15868 (2018). https://doi.org/10.1007/s00521-018-3889-z
Zhang, X., Hu, B., Zhou, L., Moore, P., Chen, J.: An EEG based pervasive depression detection for females. In: Zu, Q., Hu, B., Elçi, A. (eds.) ICPCA/SWS 2012. LNCS, vol. 7719, pp. 848–861. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37015-1_74
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Qayyum, A., Razzak, I., Mumtaz, W. (2020). Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_21
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