Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder

Brain Sciences

JamesA. Desjardins¹  Mike Cichonski¹  Lina Abou-Abbas²  Mayada Elsabbagh²  Stefonvan Noordt² 

[1] Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada;Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada;

关键词: autism spectrum disorder;    event-related potential;    empirical mode decomposition;    intrinsic mode functions;    support vector machine;    k-nearest neighbor;   

DOI  :  10.3390/brainsci11040409

来源: DOAJ

【 摘 要 】

Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.

【 授权许可】

Unknown