Olayla İlgili Potansiyel Sinyalleri Kullanarak Şizofreninin Analizi ve Sınıflandırılması

AKSÖZ, Anıl; AKYÜZ, Doğukan; BAYIR, Furkan; YILDIZ, Nevzat Can; Orhanbulucu, Fırat; Latifoğlu, Fatma

doi:10.53070/bbd.1173093

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…The studies in Vázquez et al (2021) and ( Guo et al, 2022 ), employing Butterworth and Vietoris–Rips filtering for preprocessing and RF classifier and Bottleneck and Wasserstein distances, respectively, for classification of SCZ, did not report their results in terms of accuracy. The methods in Jahmunah et al (2019) ; ( Khare and Bajaj, 2021 ; Rajesh and Sunil Kumar, 2021 ; Luján et al, 2022 ; Zandbagleh et al, 2022 ), and ( Aksöz et al, 2022 ) achieved moderate accuracy with different preprocessing techniques. However, the approaches in Neuhaus et al (2013) and ( Du et al, 2020 ) secure lower accuracies but still surpass the performance of Devia et al (2019) .…”

Section: Schizophrenia Classification Using Machine Learningmentioning

confidence: 99%

“…These studies employ diverse databases, including IPN, Kaggle SCZ, and private datasets, showcasing the adaptability of methods across various contexts. Various preprocessing techniques, such as wavelet transform ( Du et al, 2020 ), FIR filter ( Aksöz et al, 2022 ), and adaptive neuro-fuzzy inference system (ANFIS) ( Najafzadeh et al, 2021 ), highlight the richness and diversity of approaches in EEG signal analysis. Noteworthy is the study in Najafzadeh et al (2021) , which achieves a perfect accuracy of 100% using a Butterworth filter alongside ANFIS, SVM, and ANN.…”

Section: Schizophrenia Classification Using Machine Learningmentioning

confidence: 99%

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A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Rahul,

Sharma,

Sharma

et al. 2024

Front. Hum. Neurosci.

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The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI’s potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

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Section: Schizophrenia Classification Using Machine Learningmentioning

confidence: 99%

Section: Schizophrenia Classification Using Machine Learningmentioning

confidence: 99%

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Rahul,

Sharma,

Sharma

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning

Alazzawı,

Aljumaili,

Duru

et al. 2024

PeerJ Computer Science

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Schizophrenia is a severe mental disorder that impairs a person’s mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

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Olayla İlgili Potansiyel Sinyalleri Kullanarak Şizofreninin Analizi ve Sınıflandırılması

Cited by 2 publications

References 15 publications

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning

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