Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal

Krishnan, Palani Thanaraj; Raj, Alex Noel Joseph; Balasubramanian, Parvathavarthini; Chen, Yuanzhu

doi:10.1016/j.bbe.2020.05.008

Cited by 105 publications

(21 citation statements)

References 47 publications

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“…Ergonomics and biometrics, such as applications of human voice and face measurement [62]. Neuroscience [63], such as epilepsy [64], schizophrenia [65]. Customized solutions, such as health applications, monitor patient health [65] and educational applications [66].…”

Section: Applicationsmentioning

confidence: 99%

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A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Abdulrahman¹,

Baykara²

2021

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Emotion classification based on physiological signals has become a hot topic in the past decade. Many studies have attempted to classify emotions using various techniques, to discover human emotions accurately. This study focused on listing the most recent studies that have classified emotions based on electroencephalogram (EEG) signals. This study also focused on solving the problems and challenges facing researchers in emotion classification and EEG applications used in several fields. The plan of this study is based on a strategy with three aspects within specific rules: The first aspect is the methods; we chose studies that included new methods to extract features. The second aspect is the data sets. We tried to choose a study that classified the same data set. The third aspect is applications; we have listed many applications of the EEG in several areas. We concluded from this study that detecting human emotions using the EEG signals is one of the most reliable and widely used methods of detecting emotions in the past few years. Also, we have noticed that the EEG can detect human emotions, especially in psychiatry, for example, for epileptic patients whose emotions cannot be extracted using traditional methods such as facial expressions and tone of voice.

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Section: Applicationsmentioning

confidence: 99%

“…Neuroscience [63], such as epilepsy [64], schizophrenia [65]. Customized solutions, such as health applications, monitor patient health [65] and educational applications [66]. Neuromarketing is like applications that analyze customer sentiments for a particular product and not others [67].…”

Section: Applicationsmentioning

confidence: 99%

A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Abdulrahman¹,

Baykara²

2021

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“…EEG signals provide a non-invasive solution as electrical activities of the brain cannot be altered deliberately. Also, EEG signals have been widely used in the analysis of drowsiness, schizophrenia, focal, motor imagery tasks, etc [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The multiple features extracted from EEG signals have been classified by the decision tree classification method in [17]. The analysis of delta (< 4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (> 30 Hz) rhythms have been studied widely to detect the emotions in PD. In [18], the delta, theta, alpha, and beta power, and [19,20], the rhythmic study of power spectral density has been analyzed with analysis of variance (ANOVA) test.…”

Section: Introductionmentioning

confidence: 99%

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Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography

et al. 2020

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Parkinson’s disease (PD) is a severe incurable neurological disorder. It is mostly characterized by non-motor symptoms like fatigue, dementia, anxiety, speech and communication problems, depression, and so on. Electroencephalography (EEG) play a key role in the detection of the true emotional state of a person. Various studies have been proposed for the detection of emotional impairment in PD using filtering, Fourier transforms, wavelet transforms, and non-linear methods. However, these methods require a selection of basis and are confined in terms of accuracy. In this paper, tunable Q wavelet transform (TQWT) is proposed for the classification of emotions in PD and normal controls (NC). EEG signals of six emotional states namely happiness, sadness, fear, anger, surprise, and disgust are studied. Power, entropy, and statistical moments based features are elicited from the highpass and lowpass sub-bands of TQWT. Six features selected by statistical analysis are classified with a k-nearest neighbor, probabilistic neural network, random forest, decision tree, and extreme learning machine. Three performance measures are obtained, maximum mean accuracy, sensitivity, and specificity of 96.16%, 97.59%, and 88.51% for NC and 93.88%, 96.33%, and 81.67% for PD are achieved with a probabilistic neural network. The proposed method proved to be very effective such that it classifies emotions in PD and could be used as a potential tool for diagnosing emotional impairment in hospitals.

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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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Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal

Cited by 105 publications

References 47 publications

A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography

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

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