Djemal Ridha, AlSharabi Khalil, Ibrahim Sutrisno, Alsuwailem Abdullah
Electrical Engineering Department, College of Engineering, King Saud University, Box 800, Riyadh 11421, Saudi Arabia.
Biomed Res Int. 2017;2017:9816591. doi: 10.1155/2017/9816591. Epub 2017 Apr 18.
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.
自闭症谱系障碍（ASD）是一种神经发育障碍，其核心缺陷在于社会关系、沟通、想象力、思维灵活性，以及活动和兴趣范围受限。在这项工作中，研究了一种基于脑电图（EEG）信号分析的自闭症新的计算机辅助诊断（CAD）方法。所提出的方法基于离散小波变换（DWT）、熵（En）和人工神经网络（ANN）。DWT用于将EEG信号分解为近似系数和细节系数，以获得EEG子带。通过计算每个EEG子带的香农熵值来构建特征向量。ANN根据提取的特征将相应的EEG信号分类为正常或自闭症。实验结果表明了所提出方法在辅助自闭症诊断方面的有效性。使用接收者操作特征（ROC）曲线指标来量化所提出方法的性能。所提出的方法在使用沙特阿拉伯吉达阿卜杜勒阿齐兹国王医院提供的真实数据集进行测试时取得了有希望的结果。
