Classifying the Autism and Epilepsy Disorder Based on EEG Signal Using Deep Convolutional Neural Network (DCNN)

Ranjani, M; Supraja, P.

doi:10.1109/icacite51222.2021.9404634

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…In order to assess the intensity and quality of brain activity, entropy may be used as a physical metric. When the entropy is more, the brain will be more active [29].…”

Section: 𝐻(𝑋) = ∑ 𝑝(𝑥) Log 𝑝(𝑥)mentioning

confidence: 99%

Classification and Detection of Cognitive Disorders like Depression and Anxiety Utilizing Deep Convolutional Neural Network (CNN) Centered on EEG Signal

Mohan¹,

Supraja²

2023

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Electroencephalography (EEG) is a test performed to assess the electrical signals spontaneously produced during brain activities. In recent years, it is popularly used for studying both normal and pathological changes occurring in the human brain. With the World Health Organization (WHO) listing psychological disorders as a major health issue faced by the modern society, the current work focuses on this niche. It categorizes cognitive impairment like depression and anxiety using a computer-aided machine learning approach called Convolutional Neural Network. The deep CNN is trained using EEG signals from 30 patients suffering from depression and 30 others suffering from anxiety. Initially, the signal is preprocessed using Fractional Order Butterworth Filter (FOBF). The work considers the occurrence of ultra-damped, hyper-damped, and under-damped poles while designing a FOBF in a composite w-plane (w=sq; where, q is a real number). As usually executed for integer order filters in a composite w-plane, the primary initial fractional Butterworth filter is employed. The characteristics of each electrode's gamma, theta, delta, beta, alpha, and full-band EEG are then analyzed. This results in the removal of 270 nonlinear and linear characteristics. The feature space's dimensions are then reduced using a feature selection approach called Minimal-Redundancy-Maximal-Relevance (MRMR). The EEG characteristics are finally categorized by utilizing the suggested deep CNN, Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN). The accuracy of classification of the proposed approach is evaluated and found to be 97.6%. This shows it is promising for detecting depression and anxiety symptoms accurately and cost-effectively.

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“…In order to assess the intensity and quality of brain activity, entropy may be used as a physical metric. When the entropy is more, the brain will be more active [29].…”

Section: 𝐻(𝑋) = ∑ 𝑝(𝑥) Log 𝑝(𝑥)mentioning

confidence: 99%

Classification and Detection of Cognitive Disorders like Depression and Anxiety Utilizing Deep Convolutional Neural Network (CNN) Centered on EEG Signal

Mohan¹,

Supraja²

2023

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“…According to Chinese statistics, more than 30 million Chinese people suffer from depression. It is estimated that by 2021, depression will become the second leading cause of death [2].Heart disease in the world. Patients with depression will have serious psychological problems and negative emotions, usually manifested as sadness, tiredness and despair.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Mental Disorders based on EEG Signal using Deep Convolutional Neural Network

Ranjani¹,

Supraja²

2021

IRJASH

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Suicides are on the rise all across the world, and depression is a prevalent cause. As a result, effective diagnosis and therapy are required to lessen the symptoms of depression and anxiety. An electroencephalogram (EEG) is a device that measures and records electrical activity from the brain. It can be used to generate a precise assessment on the severity of depression and anxiety. Previous research has shown that EEG data and deep learning (DL) models can be used to diagnose various psychiatric disorders. As a result, this paper offers DeepNet, a DL-based convolutional neural network (CNN) foridentifying EEG data from depressed, anxiety and healthy people. This study examines DeepNet's performance in two trials, namely the subject wise split and the record wise split. DeepNet's results have an accuracy of 0.9837, and when record wise split data is used, the area under the receiver operating characteristic curve (AUC) is 0.989.

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Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

Zhang²,

Li³

2023

Journal of Neuroscience Methods

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Classifying the Autism and Epilepsy Disorder Based on EEG Signal Using Deep Convolutional Neural Network (DCNN)

Cited by 9 publications

References 17 publications

Classification and Detection of Cognitive Disorders like Depression and Anxiety Utilizing Deep Convolutional Neural Network (CNN) Centered on EEG Signal

Classification and Detection of Cognitive Disorders like Depression and Anxiety Utilizing Deep Convolutional Neural Network (CNN) Centered on EEG Signal

Diagnosing Mental Disorders based on EEG Signal using Deep Convolutional Neural Network

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

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