Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

自闭症谱系障碍 (ASD) 是一种神经和发育障碍，从儿童早期开始并持续整个人的一生。自闭症受遗传和环境因素的影响。除了其他症状外，缺乏社交互动、沟通问题以及有限的行为和兴趣可能是儿童自闭症的特征。脑电图提供有关大脑活动变化的有用信息，因此可有效地用于诊断神经系统疾病。从40名诊断为自闭症谱系障碍儿童和37名未诊断为神经发育障碍儿童的脑电信号中提取18个非线性特征。使用Student's t进行特征选择检验，并采用边际费舍尔分析进行数据缩减。这些特征是根据学生的t排序的测试。三个最重要的特征被用于制定自闭症指数，而排序的特征集被输入到 SVM 多项式 1、2 和 3 以进行分类。SVM 多项式 2 产生了 98.70% 的最高分类准确率，具有 20 个特征。开发的分类系统可能会帮助医疗保健专业人员作为检测自闭症的诊断工具。有了更多的数据，在我们未来的工作中，我们打算采用深度学习模型并探索基于云的检测系统来检测自闭症。我们的研究是新颖的，因为我们已经分析了所有非线性特征，并且我们是第一批使用提取的特征独特开发自闭症 (ASD) 指数的群体之一。