Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/538880
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dc.date.accessioned2024-01-10T12:09:07Z-
dc.date.available2024-01-10T12:09:07Z-
dc.identifier.urihttp://hdl.handle.net/10603/538880-
dc.description.abstractAutism Spectrum Disorder (ASD) is a neurodevelopmental condition characterised by abnormal brain development, which is influenced by genetic, biological, and environmental factors. Early diagnosis of ASD is crucial for timely intervention and improved long-term outcomes. However, current diagnostic methods primarily rely on behavioural observations and are subject to limitations and biases. Therefore, there is a need for alternative diagnostic approaches that are less reliant on subjective assessments. This dissertation proposes the use of Artificial Intelligence (AI)-based Computer-Aided Diagnostic Systems (CADS) for ASD diagnosis using Electroencephalography (EEG) signals and Deep Learning (DL) algorithms. The thesis investigated the effectiveness of DL classifiers in categorising individuals with ASD based on raw EEG signals without manual feature extraction newline
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dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleAnalysis of Autism Spectrum Disorder ASD by Feature Extraction of EEG Signals Using Deep Learning Algorithms
dc.title.alternative
dc.creator.researcherQaysar Mohi Ud Din
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Biomedical
dc.description.note
dc.contributor.guideJayanthy, K
dc.publisher.placeKattankulathur
dc.publisher.universitySRM Institute of Science and Technology
dc.publisher.institutionDepartment of Biomedical Engineering
dc.date.registered
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Biomedical Engineering

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01_title.pdfAttached File398.08 kBAdobe PDFView/Open
02_preliminary page.pdf523.11 kBAdobe PDFView/Open
03_content.pdf573.99 kBAdobe PDFView/Open
04_abstract.pdf335.71 kBAdobe PDFView/Open
05_chapter 1.pdf488.42 kBAdobe PDFView/Open
06_chapter 2.pdf830.42 kBAdobe PDFView/Open
07_chapter 3.pdf697.85 kBAdobe PDFView/Open
08_chapter 4.pdf671.68 kBAdobe PDFView/Open
09_chapter 5.pdf935.93 kBAdobe PDFView/Open
10_chapter 6.pdf984.14 kBAdobe PDFView/Open
11_chapter 7.pdf975.83 kBAdobe PDFView/Open
12_chapter 8.pdf478.42 kBAdobe PDFView/Open
13_annexures.pdf588.05 kBAdobe PDFView/Open
80_recommendation.pdf553.14 kBAdobe PDFView/Open


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