Autism is a developmental disorder that influences social communication skills. It is currently diagnosed only by behavioral assessment. The assessment is susceptible to the experience of the examiner as well as to the descriptive scaling standard. This paper presents a computer aided approach to discrimination between neuro-typical and autistic children. A new method- based on the computing of the elliptic area of the Continuous Wavelet Transform complex plot of resting state EEG- is presented. First, the complex values of CWT, as a function of both time and frequency, are calculated for every EEG channel. Second, the CWT complex plot is obtained by plotting the real parts of the resulted CWT values versus the related imaginary components. Third, the 95% confidence value of the elliptic area of the complex plot is computed for every channel for both autistic and healthy subjects; and the obtained values are considered as the first set of features. Fourth, three additional features are computed for every channel: the average CWT, the maximum EEG amplitude, and the maximum real part of CWT. The classification of those features is realized through artificial neural network (ANN). The obtained accuracy, sensitivity and specificity values are: 95.9%, 96.7%, and 95.1% respectively.

中文翻译：

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连续小波变换复杂图的椭圆面积基于静止状态脑电图的自闭症诊断

自闭症是一种发展障碍，会影响社交沟通技巧。目前只能通过行为评估来诊断。该评估容易受到审查员经验以及描述性缩放标准的影响。本文提出了一种计算机辅助方法来区分神经性典型儿童和自闭症儿童。提出了一种基于静态EEG的连续小波变换复图椭圆面积计算的新方法。首先，为每个EEG通道计算CWT的复数值作为时间和频率的函数。其次，通过绘制所得的CWT值的实部相对于相关虚部的图来获得CWT复杂图。第三，针对自闭症患者和健康受试者的每个通道，计算复杂曲线椭圆区域的95％置信度值；并将获得的值视为第一组特征。第四，为每个通道计算三个附加特征：平均CWT，最大EEG振幅和CWT的最大实部。这些特征的分类是通过人工神经网络（ANN）实现的。获得的准确性，敏感性和特异性值分别为：95.9％，96.7％和95.1％。这些特征的分类是通过人工神经网络（ANN）实现的。获得的准确性，敏感性和特异性值分别为：95.9％，96.7％和95.1％。这些特征的分类是通过人工神经网络（ANN）实现的。获得的准确性，敏感性和特异性值分别为：95.9％，96.7％和95.1％。