An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering

Shukla, Unmesh; Saxena, Geetika Jain; Shailani, Manish; Bafila, Anil Singh; Pundir, Amit; Singh, Sanjeev

doi:10.1109/access.2021.3133326

Cited by 7 publications

(2 citation statements)

References 27 publications

(43 reference statements)

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“…Examples include facial recognition, fig categorization, and other applications of CNN in computer vision. Like a simple neural network, CNN contains parameters that can be learned, like a neural network, namely weights, biases, etc [18].…”

Section: Eeg Classification Using Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Melinda,

Arnia,

Yafi

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Autism spectrum disorder (ASD) is a disorder of the nervous system from birth and during infancy. This disorder affects children's development, making it difficult for nerve function to develop, and causes the child concerned to have difficulty in fostering social relationships. Early detection of children with ASD is needed so that treatment is fast and on target. Currently, facilities and research on early diagnosis of ASD patients through EEG signals are still very few, requiring much cost and more effort to analyze EEG signals in examinations related to ASD detection cases. This study proposes a mobile phone application that can distinguish people living with ASD and normal data signals based on asynchronous EEG brain signals. This research also produces a preprocessing algorithm and BCI2000 EEG data signal so that it can be automated using Python. This research also produces an output model, namely the Deep Learning Convolutional Neural Network, which is deployed using Python-Flask so that the diagnosis of EEG signals with ASD and normal patients can be used on various platforms through restAPI. This research is also expected to help the community and support the diagnosis of ASD sufferers so that they can be handled appropriately. Data for ASD sufferers and normal data were correctly classified into the appropriate class. Handling this disease requires close and integrated cooperation, so this ASD classification will be very helpful for patients and can make a diagnosis in a faster time, enabling patients to receive targeted treatment and therapy.

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Section: Eeg Classification Using Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Melinda,

Arnia,

Yafi

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…It is easy to implement and has low area and power requirements [43]. The Savinsky-Golay Filter uses convolution to smooth the signal by determining the window size and polynomial-order parameters [44]. Moreover, Wiener filters eliminate the mean square error between the desired signal and its estimate [22].…”

Section: ) Semi-automated Basic Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment.Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users' cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

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Abnormal EEG Detection Using Time-Frequency Images and Convolutional Neural Network

Bajpai

Yuvaraj

Prince

et al. 2022

Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders

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An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering

Cited by 7 publications

References 27 publications

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Abnormal EEG Detection Using Time-Frequency Images and Convolutional Neural Network

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