School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, 599489 Singapore, Singapore.
Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur 613401, India.
Int J Environ Res Public Health. 2020 Feb 4;17(3):971. doi: 10.3390/ijerph17030971.
Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre-processed signals were converted to two-dimensional images using the higher-order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA). Significant features were selected from the condensed feature set using Student's -test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten-fold cross-validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism.
自闭症患者通常在表达或控制情绪以及眼神接触等方面存在困难。自闭症的发病率在全球范围内呈上升趋势，因此需要解决这一问题。目前的诊断系统存在特定的局限性；因此，一些人未被诊断出或诊断被延迟。在这项研究中，我们开发并描述了一种使用脑电图（EEG）信号的有效自闭症诊断系统，这些信号是大脑电活动产生的。预处理后的信号使用高阶谱（HOS）双谱转换为二维图像。然后提取非线性特征，并使用局部敏感判别分析（LSDA）进行降维。使用学生 t 检验从浓缩特征集中选择显著特征，然后将其输入到不同的分类器中。概率神经网络（PNN）分类器仅使用五个特征就实现了 98.70%的最高准确率。采用 10 倍交叉验证来评估分类器的性能。结果表明，所开发的系统可以作为决策支持工具，帮助医疗保健专业人员诊断自闭症。
