دانشگاه کاشان محاسبات نرم 2322-3707 مقالات آماده انتشار 2024 01 30 Autism Diagnosis from EEG Signals Using Machine Learning Algorithms and Convolutional Neural Networks Autism Diagnosis from EEG Signals Using Machine Learning Algorithms and Convolutional Neural Networks 114266 10.22052/scj.2024.253566.1185 FA زهره شریفی مهرجرد گروه مهندسی برق، دانشگاه فنی و حرفه ای، تهران، ایران هاجر مومنی گروه مهندسی برق، دانشگاه اردکان، اردکان، ایران حبیب ادبی اردکانی گروه مهندسی برق، دانشگاه یزد، یزد، ایران Journal Article 2023 09 10 A neurodevelopmental disorder recognized by insufficiency in social communication and repetitive behaviours is expressed as an autism spectrum disorder (ASD). One of the most useful tools for diagnosing autism is the use of electroencephalography (EEG) signals because these signals accurately represent the brain's function. The recorded EEG of each person contains a lot of information that is very difficult to study and check visually. The main goal of machine learning algorithms is to train the machine in such a way that it finally has a diagnosis close to that of the human brain. This paper evaluates the appropriate strategies for further exploitation of deep learning capabilities in the feature extraction block of autism diagnosis without using classical feature extraction methods. In this research, a convolutional neural network (CNN) structure is used to check the available data in order to extract features. Classification has been done with five machine learning classifiers, covering support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT), simple Bayes classification (GNB), and random forest (RF). The accuracy obtained from the use of classifiers with SVM methods is 100%, LDA 82%, DT 80.5%, GNB 100%, and RF 100%. The proposed idea of convolutional neural networks for feature extraction and classification with different machine learning methods has provided high-accuracy results that are equal to the other amazing methods for autism diagnosis. A neurodevelopmental disorder recognized by insufficiency in social communication and repetitive behaviours is expressed as an autism spectrum disorder (ASD). One of the most useful tools for diagnosing autism is the use of electroencephalography (EEG) signals because these signals accurately represent the brain's function. The recorded EEG of each person contains a lot of information that is very difficult to study and check visually. The main goal of machine learning algorithms is to train the machine in such a way that it finally has a diagnosis close to that of the human brain. This paper evaluates the appropriate strategies for further exploitation of deep learning capabilities in the feature extraction block of autism diagnosis without using classical feature extraction methods. In this research, a convolutional neural network (CNN) structure is used to check the available data in order to extract features. Classification has been done with five machine learning classifiers, covering support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT), simple Bayes classification (GNB), and random forest (RF). The accuracy obtained from the use of classifiers with SVM methods is 100%, LDA 82%, DT 80.5%, GNB 100%, and RF 100%. The proposed idea of convolutional neural networks for feature extraction and classification with different machine learning methods has provided high-accuracy results that are equal to the other amazing methods for autism diagnosis. https://scj.kashanu.ac.ir/article_114266_0fb2e920a9eff5b81d28061ecbad6ec1.pdf