Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions

Piryatinska, Alexandra; Darkhovsky, B. S.; Kaplan, Arline

doi:10.1016/j.cmpb.2017.09.001

Cited by 63 publications

(35 citation statements)

References 24 publications

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“…Then they classified these coefficients with Random Forest (RF) and support vector machine (SVM). Table III shows a comparison between the accuracy reached by [12] and the proposed method, Piryatinska et al [12] reached an accuracy of 84.5% applying RF on -complexity coefficients, and 81.07% utilizing SVM on -complexity coefficients, lower than the accuracy of 90% obtained by our method applying CNN on the channels correlation matrix obtained from raw EEG signals. V. CONCLUSION…”

Section: B Resultsmentioning

confidence: 60%

“…Piryatinska et al [12] presented a model that calculates -complexity coefficients of the original signal. Then they classified these coefficients with Random Forest (RF) and support vector machine (SVM).…”

Section: B Resultsmentioning

confidence: 99%

“…EEG recordings data used for this work, also analyzed in [12], is available at http://brain.bio.msu.ru/eeg schizophrenia. htm.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…The first one consists of 39 healthy boys (11-13 years old). The second consists of 45 boys (10-14 years old) diagnosed with schizophrenia using clinical interviews at the Research Center for Psychological Disorders of the Russian Academy of Medical Sciences [12]. For all subjects, from 16 channels (electrode positions) the EEG signals were registered, those channels are F7, F3, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…An EEG provides information about the electrical activity of the brain [6], [7], with this information is possible to identify emotions [8], [9], [10], [11], whether a person is healthy or suffer some neurological disease [12], [13], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Naira¹,

Alamo²

2019

IJACSA

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More than 21 million people worldwide suffer from schizophrenia. This serious mental disorder exposes people to stigmatization, discrimination, and violation of their human rights. Different works on classification and diagnosis of mental illnesses use electroencephalogram signals (EEG) because it reflects brain functioning, and how these diseases affect it. Due to the information provided by the EEG signals and the performance demonstrated by Deep Learning algorithms, the present work proposes a model for the classification of schizophrenic and healthy people through EEG signals using Deep Learning methods. Considering the properties of an EEG, high-dimensional and multichannel, we applied the Pearson Correlation Coefficient (PCC) to represent the relations between the channels, this way instead of using the large amount of data that an EEG provides, we used a shorter matrix as an input of a Convolutional Neural Network (CNN). Finally, results demonstrated that the proposed EEG-based classification model achieved Accuracy, Specificity, and Sensitivity of 90%, 90%, and 90%, respectively.

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Section: B Resultsmentioning

confidence: 60%

Section: B Resultsmentioning

confidence: 99%

“…EEG recordings data used for this work, also analyzed in [12], is available at http://brain.bio.msu.ru/eeg schizophrenia. htm.…”

Section: A Data Descriptionmentioning

confidence: 99%

Section: A Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Naira¹,

Alamo²

2019

IJACSA

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Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks

Polat

2023

J Applied Clin Med Phys

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The complexity of symptoms of schizophrenia (SZ) complicate traditional and effective diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time‐consuming, and error‐prone. Thus, there is a requirement to develop automated systems for timely and accurate diagnosis of SZ. This paper proposes an automated SZ diagnosis pipeline based on residual neural networks (ResNet). To exploit the superior image processing capabilities of the ResNet models, multi‐channel electroencephalogram (EEG) signals were converted into functional connectivity representations (FCRs). The functional connectivity of multiple regions in the cerebral cortex is critical for a better understanding of the mechanisms of SZ. In creating the FCR input images, the phase lag index (PLI) was calculated based on 16‐channel EEG signals from 45 SZ patients and 39 healthy control (HC) subjects to reduce and avoid the volume conduction effect. The experimental results showed that satisfactory classification performance (accuracy = 96.02%, specificity = 94.85%, sensitivity = 97.03%, precision = 95.70%, and F1‐score = 96.33%) was achieved by combining FCR inputs of beta oscillatory and the ResNet‐50 model. The statistical analyses also confirmed that there is a significant difference between SZ patients and HC subjects (p < 0.001, one‐way ANOVA). More specifically, the average connectivity strengths between nodes in the parietal cortex and those in the central, occipital, and temporal regions were significantly reduced in SZ patients compared to HC subjects. Overall results demonstrated that this paper not only provided an automated diagnostic model whose classification performance is superior to most previous studies but also valuable biomarkers for clinical use.

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Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Nsugbe¹

2023

Applied AI Letters

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Schizophrenia is a psychiatric disorder which is prevalent in individuals around the world, where diagnosis methods for this disorder are done via a combination of interview style questioning of the patient alongside a review of their medical record; but these methods have been largely criticised for being subjective between psychiatrists and largely unreplicable. Schizophrenia also occurs in adolescent individuals who have been said to be even more challenging to diagnose largely due to delusions being mistaken for childhood fantasies, and established methods for adult patients being applied to diagnose adolescents. This work investigates the use of electroencephalography (EEG) signals acquired from adolescent patients in the age range of 10-14 years, alongside signal processing methods and machine learning modelling towards the diagnosis of adolescent schizophrenia. The results from the machine learning modelling showed that the linear discriminant analysis (LDA) and fine K-nearest neighbour (KNN) produced the best recognition results for models with easy and hard interpretability, respectively. Additionally, a computational method was applied towards contrasting the neuroanatomical activation patterns in the brain of the schizophrenic and normal adolescents, where it was seen that the neural activation patterns of the normal adolescents showed a greater consistency when compared with the schizophrenics.

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Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions

Cited by 63 publications

References 24 publications

Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks

Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

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