Exploring deep residual network based features for automatic schizophrenia detection from EEG

BetterHealth channel, https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/schizophrenia. Healthdirect 2018, Schizophrenia, Healthdirect, viewed https://www.healthdirect.gov.au/schizophrenia. Accessed 4 Feb 2020 World Health Organization (WHO) 2019, schizophrenia, WHO, viewed https://www.who.int/news-room/fact-sheets/detail/schizophrenia. Accessed 5 Feb 2020 Goshvarpour A, Goshvarpour A (2020) Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Phys Eng Sci Med 43:227–238. https://doi.org/10.1007/s13246-019-00839-1 PE Tibbetts, (2013) Principles of cognitive neuroscience. Second Edition /Principles of neuroscience. Fifth Edition. Q Rev Biol pp. 88 139–140 McGlashan TH (1998) Early detection and intervention of schizophrenia: rationale and research. Br J Psychiatry 172:3–6 Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci 9(14):2870 Alvi MA, Siuly S, Wang H, Wang K, Whittaker F (2022) A Deep learning based framework for diagnosis of mild cognitive impairment. Knowledge based Syst. https://doi.org/10.1016/j.knosys.2022.108815 Talo M, Baloglu UB, Yıldırım Ö, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 54:176–188 Gudigar A, Raghavendra U, San TR, Ciaccio EJ, Acharya UR (2019) Application of multiresolution analysis for automated detection of brain abnormality using MR images: a comparative study. Future Gener Comput Syst 90:359–367 Acharya UR, Sree SV, Ang PCA, Yanti R, Suri JS (2012) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22(2):1250002 S Siuly, Y Li, Y Zhang (2016) EEG Signal analysis and classification: techniques and applications. Health Information Science, Springer Nature, US (ISBN 978-3-319-47653-7). Sadiq MT, Akbari H, Siuly S, Yousaf A, Rehman AU (2021) A novel computer-aided diagnosis framework for EEG-based identification of neural diseases. Comput Biol Med 138:104922 Alvi AM, Siuly S, Wang H (2022) Neurological abnormality detection from electroencephalography data: a review. Artif Intell Rev 55:2275–2312. https://doi.org/10.1007/s10462-021-10062-8 Ruiz J, de Miras AJ, Ibáñez-Molina MFS, Iglesias-Parro S (2023) Schizophrenia classification using machine learning on resting state EEG signal. Biomedical Signal Process Control. https://doi.org/10.1016/j.bspc.2022.104233 Akbari H, Ghofrani S, Zakalvand P, Tariq Sadiq M (2021) Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed Signal Process Control 69:102917 E Olejarczyk, W (2017) Jernajczyk EEG in Schizophrenia. RepOD Olejarczyk E, Jernajczyk W (2017) Graph-based analysis of brain connectivity in schizophrenia. PLoS ONE 12:e0188629 Kim K, Duc NT, Choi M, Lee B (2021) “EEG microstate features for schizophrenia classification. PLoS ONE 16:e0251842 R Buettner, D Beil, S Scholtz, and A Djemai, (2020) Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings. In: HICSS-53 proceedings: 53rd Hawaii International conference on system sciences. Krishnan PT, Joseph Raj AN, Balasubramanian P, Chen Y (2020) Schizophrenia detection using multivariate empirical mode decomposition and entropy measures from multichannel EEG signal. Biocybern Biomed Eng 40:1124–1139 Jahmunaha V, Oha SL, Rajinikanthb V, Ciaccioe EJ, Cheongf KH, Arunkumarh N, Acharyaa UR (2019) Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med 100:101698 F Li, J Wang, Y Liao, C Yi, Y Jiang, Y Si, W Peng, D Yao, Y Zhang, W Dong,P Xu, (2019) Differentiation of Schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE transactions on neural systems and rehabilitation engineering, pp. 27 594–602 Ko D-W, Yang J-J (2022) EEG-based schizophrenia diagnosis through time series image conversion and deep learning. Electronics 11(2265):2022 Kaggle website, https://www.kaggle.com/broach/button-tone-sz. Aslan Z, Akin M (2020) Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals. Trait Du Signal 2020(37):235–244 Borisov SV, Kaplan AY, Gorbachevskaya NL, Kozlova IA (2005) Analysis of EEG structural synchrony in adolescents with schizophrenic disorders. Hum Physiol 31:255–261 Phang C-R, Ting C-M, Samdin SB, Ombao H (2019) Classification of eeg-based effective brain connectivity in schizophrenia using deep neural networks. 9th International IEEE/EMBS conference on neural engineering (NER). IEEE, San Francisco, pp 401–406 Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E (2022) A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 16:926426. https://doi.org/10.3389/fnins.2022.926426 Center for Biomedical Research Excellence (COBRE); COBRE data set available. http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html Ghanbari M, Pilevar AH, Bathaeian N (2022) Diagnosis of schizophrenia using brain resting-state fMRI with activity maps based on deep learning. SIViP. https://doi.org/10.1007/s11760-022-02229-9 Zheng J, Wei X, Wang J, Lin H, Pan H, Shi Y (2021) Diagnosis of schizophrenia based on deep learning using fMRI. Comput Math Methods Med 9:8437260. https://doi.org/10.1155/2021/8437260.PMID:34795793;PMCID:PMC8594998 Oh J, Oh B-L, Lee K-U, Chae J-H, Yun K (2020) Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psychiatry 1(1):16 Lei D, Pinaya WHL, Young J, van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Vieira S, Huang X et al (2020) Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp 41:1119–1135 K He, X Zhang, S Ren, J Sun, (2016) Deep residual learning for image recognition. In: CVPR. Qin FW, Gao NN, Peng Y, Wu ZZ, Shen SY, Grudtsin A (2018) Finegrained leukocyte classification with deep residual learning for microscopic images. Comp Methods Programs Biomed 162(8):243–252 Surinta O, Khamket T (2019) Recognizing pornographic images using deep convolutional neural networks. The 4th International conference on digital arts, media and technology and 2nd ECTI Northern section conference on electrical, electronics, computer and telecommunications engineering. IEEE, Nan, pp 150–154 Lee S, Jang G (2017) Recognition model based on residual networks for cursive hanja recognition. International conference on information and communication technology convergence (ICTC). IEEE, Jeju, pp 579–583 B Liu, K Yao, M Huang, J Zhang, Y Li, R Li (2018) Gastric pathology image recognition based on deep residual networks. In: IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, pp. 408–412. D Lu, J Triesch, “Residual deep convolutional neural network for EEG signal classification in epilepsy”, https://arxiv.org/abs/1903.08100. RK Srivastava, K Greff, J Schmidhuber. (2015) Highway networks. arXiv:1505.00387 [cs.LG]. X Yu, Z Yu, S Ramalingam, (2018) “Learning Strict Identity Mappings in Deep Residual Networks”. arXiv:1804.01661 [cs.CV]. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. European conference on computer vision. Springer International Publishing, Cham, pp 630–645 Zeng H, Yang C, Dai G, Qin F, Zhang J, Kong W (2018) EEG classification of driver mental states by deep learning. Cogn Neurodyn 12:597–606 Bridle JS (1990) Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulié FF, Hérault J (eds) Neurocomputing. Springer, Heidelberg, pp 227–236 G Cai, Y Guo, W Chen, H Zeng, Y Zhou, Y Lu. (2019) 14-Neutrosophic set based deep learning in mammogram analysis. Neutrosophic Set in Medical Image Analysis, pp. 287-310 Vapnik V (2000) The nature of statistical learning theory. Springer-Verlag, New York Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inform Theory 13(1):21–27 Siuly S, Bajaj V, Sengur A, Zhang Y (2019) An advanced analysis system for identifying alcoholic brain state through EEG signals. Int J Autom Comput 16:737–747 Siuly S, Li Y (2014) A novel statistical framework for multiclass EEG signal classification. Eng Appl Artif Intell 34:154–167 Supriya S, Siuly S, Wang H, Zhang Y (2018) EEG Sleep stages analysis and classification based on weighed complex network features. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2018.2876529 Siuly Y (2015) Li, “Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification.” Comput Methods Programs Biomed 119:29–42 Ford JM, Palzes VA, Roach BJ, Mathalon DH (2014) Did I do that? Abnormal predictive processes in schizophrenia when button pressing to deliver a tone. Schizophr Bull 40(4):804–812 Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y (2020) A Computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural syst Rehabilitation Eng 28(11):2390–2400. https://doi.org/10.1109/TNSRE.2020.3022715 Khare SK, Bajaj V, Siuly S, Sinha GR (2020) Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals. In: Bajaj V, Sinha GR (eds) Modelling and analysis of active biopotential signals in healthcare, vol 1. IOP Science, Bristol, pp 1–26. https://doi.org/10.1088/978-0-7503-3279-8ch1 Khare SK, Bajaj V (2021) A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Programs Biomed 211:106450 Khare SK, Bajaj V (2022) A hybrid decision support system for automatic detection of schizophrenia using EEG signals. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2021.105028 Khare SK, Bajaj V, Acharya UR (2021) SPWVD-CNN for automated detection of schizophrenia patients using EEG signals. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2021.3070608