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Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification

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Abstract

In this paper, deep-stacked error minimized extreme learning machine autoencoder (DSEMELMAE) and sine–cosine monarch butterfly optimization-based minimum variance multikernel random vector functional link network are integrated to recognize the schizophrenia electroencephalogram (EEG) data. The unconventional DSEMELMAE network is modelled to derive very unique unsupervised attributes out of the brain signals and employ as inputs to the proposed supervised SCAMBO-MVMKRVFLN classification methodology to recognize accurately by minimizing the mean-square error for identifying schizophrenia data with encouraging accuracy. The DSEMELMAE-SCAMBO-MVMKRVFLN integrated approach is assessed over benchmark EEG databases. The proposed approach is compared with many related RVFLN-based deep learning approaches and many state-of-the-art methods and found to be the outperformer among all the methods, and this approach is highly accepted owing to faster learning speed, better computational simplicity, good generalization capability, outstanding classification accuracy, and small event identification time. The classifier MVMKRVFLN is unique as it classifies the signal with advantages such as the regularization of the randomization, computational economy, less training expenses, the direct inverse along with minimum reconstruction error. The KRVFLN uses multiple kernels such as wavelet, tan hyperbolic and multiquadric to improve the classification performance. The effectiveness of the proposed method is verified by examining three publicly available schizophrenic EEG datasets such as Poland, Kaggle and Moscow datasets and achieved classification accuracies with 99.989%, 95.012% and 96.69%, respectively. The recognition capability, simplicity and robustness of the proposed methodology prove the outstanding overall performances of schizophrenia recognition and diagnosis in comparison with other state-of-the-art approaches and different learning approaches.

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Abbreviations

DSEMELMAE:

Deep-stacked error minimized extreme learning machine autoencoder

SCMBO-MVMKRVFLN:

Sine–cosine monarch butterfly optimization-based minimum variance multikernel random vector functional link network

SZ:

Schizophrenia

EEG:

Electroencephalogram

EMD:

Empirical mode decomposition

SVM:

Support vector machine

CNN:

Convolution neural network

CAD:

Computer-aided diagnosis

LSTM:

Long short time memory

RVMD-OELM:

Robust variational mode decomposition-based optimised extreme learning machine

ANN:

Artificial neural network

NB:

Naïve Bayes

DT:

Decision tree

SLFN:

Single-layer feedforward neural network

ELM:

Extreme learning machine

EMELM:

Error minimized extreme learning machine

DBN:

Deep belief networks

SAE:

Deep-stacked autoencoder

DBN:

Deep belief networks

DSSAE:

Deep-stacked sparse autoencoder

RVFLN:

Random vector functional link network

MVMKRVFLN:

Multikernel random vector functional link network

HC:

Healthy controls

BL:

Best fit individual butterflies

WCA:

Water cycle algorithm

Se:

Sensitivity

Sp:

Specificity

Ac:

Accuracy

Rfp/hr:

False positive rate per hour

Fm:

F-measure

Tp:

True positive

Tn:

True negative

Fn:

False negative

Fp:

False positive

SCAMBO:

Sine-cosine monarch butterfly optimization

DSEMELMAE-SCAMBO-MVMKRVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with sine-cosine monarch butterfly optimization-based minimum variance multikernel-based random vector functional link network

DSEMELMAE-MVMKRVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with minimum variance multikernel random vector functional link network

DSEMELMAE-MVKRVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with minimum variance kernel-based random vector functional link network

DSEMELMAE-MVRVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with minimum variance random vector functional link network

DSEMELMAE-KRVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with kernel-based random vector functional link network

DSEMELMAE-RVFLN:

Deep-stacked error minimized extreme learning machine autoencoder with random vector functional link network

DSELMAE-MVMKRVFLN:

Deep-stacked extreme learning machine autoencoder with minimum variance multikernel-based random vector functional link network

DSELMAE-MVKRVFLN:

Deep-stacked extreme learning machine autoencoder with minimum variance kernel-based random vector functional link network

DSELMAE-MVRVFLN:

Deep-stacked extreme learning machine autoencoder with minimum variance random vector functional link network

DSAE-MVMKRVFLN:

Deep-stacked autoencoder with minimum variance multikernel-based random vector functional link network

RBPNN:

Radial basis probabilistic neural network

CO:

Chimp optimization

RFO:

Red fox optimization

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Authors and Affiliations

  1. Siksha O Anusandhan Deemed to be University, Bhubaneswar, India

    Sebamai Parija, Mrutyunjaya Sahani, Ranjeeta Bisoi & P. K. Dash

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  1. Sebamai Parija
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  2. Mrutyunjaya Sahani
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  3. Ranjeeta Bisoi
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  4. P. K. Dash
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Correspondence to P. K. Dash.

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Parija, S., Sahani, M., Bisoi, R. et al. Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification. Pattern Anal Applic 26, 403–435 (2023). https://doi.org/10.1007/s10044-022-01107-x

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