A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory

Caroline W. Cognition and social behavior in schizophrenia: an animal model investigating the potential role of nitric oxide. Sweden Institute of Neuroscience and Physiology; 2007.

Guze SB. Diagnostic and statistical manual of mental disorders: DSM- IV. Washington DC: American Psychiatric Association; 1994.

Google Scholar 

World Health Organization. International Statistical Classification of Diseases and Health Related Problems ICD-10. 2005. https://apps.who.int/iris/handle/10665/43110

Centorrino F, Baldessarini RJ, Price BH, Tuttle M, Bahk WM, Hennen J. EEG abnormalities during treatment with typical and atypical antipsychotics. Am J Psychiatry. 2002;159(1):109–15.

Article  Google Scholar 

Keshayan MS, Diwadkar VA, Montrose DM, Rajarethinam R, Sweeney JA. Premorbid indicators and risk for schizophrenia: a selective review and update. Schizophr Res. 2005;79(1):45–57.

Article  Google Scholar 

Tandon R, Nasrallah HA, Keshavan MS. Schizophrenia, just the facts Clinical features and conceptualization. Schizophr Res. 2009;110:1–23.

Article  Google Scholar 

Marwaha S, Johnson S. Schizophrenia and employment-a review. Soc Psychiatry Psychiatr Epidemiol. 2004;39:337–49.

Article  Google Scholar 

Andreasen NC. Scale for the assessment of thought, language, and communication (TLC). Schizophr Bull. 1976;12:473–82.

Article  Google Scholar 

Choi HS, Lee B, Yoon S. Biometric authentication using noisy electrocardiograms acquired by mobile sensors. IEEE Access. 2016;4:1266–73.

Article  Google Scholar 

Guger C, Schlogl A, Neuper C, Walterspacher D, Strein T, Pfurtscheller G. Rapid prototyping of an EEG-based BCI. IEEE Trans Neural Syst Rehab Eng. 2001;9:49–58.

Article  Google Scholar 

Panayiotopoulos CP. EEG and brain imaging. In: A clinical guide to epileptic syndromes and their treatment. London: Springer; 2010.

Knyazeva MG, Innocenti GM. EEG coherence studies in the normal brain and after early-onset cortical pathologies. Brain Res Rev. 2001;36:119–28.

Article  Google Scholar 

Guevara MA, Lorenzo I, Arce C, Ramos J, Corsi-Cabrera M. Inter-and intrahemispheric EEG correlation during sleep and wakefulness. Sleep. 1995;18:257–65.

Article  Google Scholar 

Hornero R, Abasolo D, Jimeno N, Sa´nchez CI, Poza J, Aboy M. Variability, regularity and complexity of time series generated by schizo-phrenic patients and control subjects. IEEE Trans Biomed Eng. 2006;53:210–8.

Article  Google Scholar 

Sabeti M, Boostani R, Katebi SD, Price GW. Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed Signal Process Control. 2007;2:122–34.

Article  Google Scholar 

Kim DJ, Jeong J, Chae JH, Park S, Kim SY, Go HJ. An estimation of the first positive Lyapunov exponent of the EEG in patients with schizophrenia. Psychiatry Res Neuroimaging. 2009;98(3):177–89.

Article  Google Scholar 

Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med. 2009;47:263–74.

Article  Google Scholar 

Kim JW, Lee YS, Han DH, Min KJ, Lee J, Lee K. Diagnostic utility of quantitative EEG in un-medicated schizophrenia. Neurosci Lett. 2015;589:126–31.

Article  Google Scholar 

Dvey Z, Fogelson N, Peled A, Intrator N. Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach. PLoS ONE. 2015;10:1–12.

Google Scholar 

Santos-Mayo L, San-José-Revuelta LM, Arribas JI. A computer-aided diagnosis system with EEG based on the P3b wave during an auditory Odd-Ball task in schizophrenia. IEEE Trans Biomed. 2017;64:395–407.

Article  Google Scholar 

Patel R, Gireesan K, Baskaran R, Shekar N. Optimal classification of N-back task EEG data by performing effective feature reduction. Sådhanå. 2022. https://doi.org/10.1007/s12046-022-02015-w.

Article  Google Scholar 

Shu LO, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR. Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci. 2019;9:1–13.

Google Scholar 

Phang CR, Noman F, Hussain H, Ting CM, Ombao H. A multi-domain connectome convolutional neural network for identifying schizophrenia from EEG connectivity patterns. IEEE J Biomed Health Inf. 2019;24(5):1333–43.

Article  Google Scholar 

Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med. 2021;43:1229–39.

Article  Google Scholar 

Chandran C, Sreekumar K, Subha DP. EEG-based automated detection of schizophrenia using long short-term memory (LSTM) network. In: Advances in machine learning and computational intelligence. Springer, Singapore; 2021. pp. 229–236

Aslan Z, Akin M. A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med. 2022;45:83–96.

Article  Google Scholar 

Sun J, Cao R, Zhou M, Hussain W, Wang B, Xue J, Xiang J. A hybrid deep neural network for classification of schizophrenia using EEG data. Sci Rep. 2021;11(1):1–16.

Google Scholar 

Gorbachevskaya NN, Borisov S. EEG data of healthy adolescents and adolescents with symptoms of schizophrenia. Available via http://brain.bio.msu.ru/eeg_schizophrenia.htm.

Litjens G, Ciompi F, Wolternik J, Vos B, Leiner T, Teuwen J, Isgum I. State of the art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging. 2019;12:1549–65.

Article  Google Scholar 

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems, Montreal, QC, Canada. 2014; pp. 2672–2680

Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. 2015. arXiv:1511.06434

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015. arXiv:1502.03167

Xu B, Wang N, Chen T, Li M. Empirical evaluation of rectified activations in convolutional network. 2015. arXiv:1505.00853.

Chen Y, Jiang H, Li C, Jia X, Ghamisi P. Deep feature extraction and classification of hyper spectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens. 2016;54(10):6232–51.

Article  Google Scholar 

Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw. 1994. https://doi.org/10.1109/72.279181.

Article  Google Scholar 

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997. https://doi.org/10.1162/neco.1997.9.8.1735.

Article  Google Scholar 

Comments (0)

No login
gif