Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Puthankatti SD, Adeli A (2015) A novel depression diagnosis index using nonlinear features in EEG signals. Eur Neurol 74(1–2):79–83. https://doi.org/10.1159/000438457

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018a) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018b) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 161:103–113

Afshani F, Shalbaf A, Shalbaf R, Sleigh J (2019) Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn Neurodyn 13(6):531–540

Ahmadlou M, Adeli H, Adeli A (2012) Fractality analysis of the frontal brain in major depressive disorder. Int J Psychophysiol 85:206–211

Ahmadlou M, Adeli H, Adeli A (2013) Spatiotemporal analysis of relative convergence of EEGs reveals differences between brain dynamics of depressive women and men. Clin EEG Neurosci 44:175–181

Association AP (2000) Diagnostic and Statistical manual of mental disorders: DSM-IV-TR®. American Psychiatric Publishing, Washington, DC

Astolfi L, Cincotti F, Mattia D, Marciani MG (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28(2):143–157

Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, Acharya UR (2019) Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 43(7):205. https://doi.org/10.1007/s10916-019-1345-

Baccalá LA, Sameshima K (2001) Partial directed coherence: a new concept in neural structure determination. Biol Cybern 84:463–474

Baccalá LA, Sameshima K (2007) Generalized partial directed coherence. In: 15th International conference on digital signal processing, pp 163–166. IEEE

Bachmann M, Lass J, Hinrikus H (2017) Single channel EEG analysis for detection of depression. Biomed Signal Process Control 31:391–397

Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network-based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19(12):4494–4500

Chauhan R, Ghanshala KK, Joshi RC (2018) Convolutional neural network (CNN) for image detection and recognition. In: 2018 First international conference on secure cyber computing and communication (ICSCCC), Jalandhar, India, pp 278–282

Čukić M, Stokić M, Simić S, Pokrajac D (2020) The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn 14(4):443–455

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

Faust O, Ang PCA, Puthankattil SD, Joseph PK (2014) Depression diagnosis support system based on EEG signal entropies. J Mech Med Biol 14(3):1450035

Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed 161:1–3

Geweke JF (1984) Measures of conditional linear dependence and feedback between time series. J Am Stat Assoc 79(388):907–915

Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438

Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 6(02):107–116

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

Hosseinifard B, Moradi MH, Rostami R (2013) classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed 109:339–345

Kaminski M, Ding M, Truccolo WA, Bressler SL (2001) Evaluating causal relations in neural systems: Granger causality, directed transfer function and Statistical assessment of significance. Biol Cybern 85:145–157

Li X, Hu B, Sun S, Cai H (2016) EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed 136:151–161

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

Luan Y, Lin S (2019) Research on text classification based on CNN and LSTM. In: 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), pp 352–355. IEEE

Mullen T (2010) An electrophysiological information flow toolbox for EEGLAB theoretical handbook and user manual

Mumtaz W, Qayyum A (2019) A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform 132:103983

Mumtaz W, Xia LK, Ali SSA, Yasin MAM, Hussain M, Malik AS (2017a) Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed Signal Process Control 31:108–115

Mumtaz W, Xia L, Mhod Yasin MA, Azhar Ali SS, Malik AS (2017b) A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLoS ONE 12(2):e0171409. https://doi.org/10.1371/journal.pone

Nagabushanam P, George ST, Radha S (2019) EEG signal classification using LSTM and improved neural network algorithms. Soft Comput 24:9981–10003

Puthankattil SD, Joseph PK (2012) Classification of EEG signals in normal and depression conditions by ANN using RWE and signal entropy. J Mech Med Biol 12:1240019

Raghavendra U, Acharya UR, Adeli H (2019) Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol 82(1–3):41–64

Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng 16(5):051001

Shalbaf A, Saffar M, Sleigh JW, Shalbaf R (2017) Monitoring the depth of anesthesia using a new adaptive neurofuzzy system. IEEE J Biomed Health Inform 22(3):671–677

Shalbaf A, Shalbaf R, Saffar M, Sleigh J (2019) Monitoring the level of hypnosis using a hierarchical SVM system. J Clin Monit Comput 15:1–8

Sharma M, Achuth PV, Deb D, Puthankattil SD, Acharya UR (2018) An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. Cogn Syst Res 52:508–520

Subhani AR, Kamel N, Mohamad Saad MN et al (2018) Mitigation of stress: new treatment alternatives. Cogn Neurodyn 12:1–20

Sun W, Tseng TL, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 57:4–9

Wang Y, Xuying X, Zhu Y, Wang R (2019) Neural energy mechanism and neurodynamics of memory transformation. Nonlinear Dyn 97:697–714

World Federation for Mental Health (2012) Depression: a global crisis, Occoquan, VA, USA

World Health Organization (2017) Depression. http://www.who.int/mediacentre/factsheets/fs369/en/

Yao D, Zhang Y, Liu T, Xu P, Gong D, Lu J, Xia Y, Luo C, Guo D, Dong L, Lai Y, Chen k, Li J (2020) Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations. Cogn Neurodyn 14(4):425–441. https://doi.org/10.1007/s11571-020-09577-7

Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya R (2019) Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Comput Biol Med 113:103387

Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D (2019) A survey on deep learning based brain computer interface: recent advances and new frontiers. arXiv preprint arXiv:1905.04149