Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Keihani, Ahmadreza; Sajadi, Seyed Saman; Hasani, Mahsa; Ferrarelli, Fabio

doi:10.3390/brainsci12111497

Cited by 16 publications

(3 citation statements)

References 69 publications

(121 reference statements)

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“…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. The methods in Shim et al (2016) ; ( Jeong et al, 2017 ; Lai et al, 2019 ; Kim et al, 2020 , 2021 ; Azizi et al, 2021 ; Keihani et al, 2022 ), and ( Santos-Mayo et al, 2016 ), which use a bandpass filter in combination with other preprocessing techniques, fail to achieve the impressive accuracy seen in Aydemir et al (2022) ; ( Agarwal and Singhal, 2023 ), and ( Najafzadeh et al, 2021 ). 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.…”

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.

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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%

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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“…These microstates, C and D, have emerged as potential endophenotypes for schizophrenia[ 20 ]. The utilization of these microstates in the clinical diagnosis and treatment of schizophrenia has reached a significant level of consensus among various studies[ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function

Peng,

Fan,

et al. 2024

World J Psychiatry

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BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography (EEG) in people with depression. However, the consistency of findings on EEG microstates in patients with depression is poor, and few studies have reported the relationship between EEG microstates, cognitive scales, and depression severity scales. AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions. METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We collected information relating to demographic and clinical characteristics, as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Chinese version) and EEG. RESULTS Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher [depression (DEP): Duration 84.58 ± 24.35, occurrence 3.72 ± 0.56, contribution 30.39 ± 8.59; CON: Duration 72.77 ± 10.23, occurrence 3.41 ± 0.36, contribution 24.46 ± 4.66; Duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; Contribution F = 10.82, P = 0.011] while the duration, occurrence, and contribution of microstate D were significantly lower (DEP: Duration 70.00 ± 15.92, occurrence 3.18 ± 0.71, contribution 22.48 ± 8.12; CON: Duration 85.46 ± 10.23, occurrence 3.54 ± 0.41, contribution 28.25 ± 5.85; Duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in patients with depression. A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B (r = 0.405, P = 0.049). CONCLUSION EEG microstate, especially C and D, is a possible biomarker in depression. Patients with depression had a more frequent transition from microstate C to B, which may relate to more negative rumination and visual processing.

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“…EEG microstate representations provide a tool to analyze the temporal dynamics of whole-brain neuronal networks. Microstate analysis has been shown to be capable to diagnose schizophrenia [1] [2], epilepsy [3], Alzheimer's disease and early dementia [4]. However, the study of differences between the microstate characteristics of thought processes remain limited.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks

Vetró¹,

Hullám²

2023

Proceedings of the 30th Minisymposium

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Microstate analysis of EEG recordings has long been an instrumental tool for studying the temporal dynamics of whole-brain neuronal networks. The characteristics of EEG microstate sequences have been used mostly for diagnostic purposes, including the detection of schizophrenia, epilepsy, Alzheimer's disease, and early dementia. Aside from diagnostics, the use of this methodology has been limited. In this study, we examine the discriminative power of EEG microstates to differentiate between mental tasks, and we assess the generalizing power of microstate representations over different subjects and recording sessions. For this purpose, we inspect both the characteristics of discrete microstate sequences, as well as various features generated from the association of detected microstates with the continuous EEG data. For demonstration purposes, we use two distinct datasets, which contain recordings from multiple subjects, while performing different mental tasks.

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Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Cited by 16 publications

References 69 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

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function

Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks

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