Application of continuous wavelet transform and support vector machine for autism spectrum disorder electroencephalography signal classification

The article’s subject matter is to classify Electroencephalography (EEG) signals in Autism Spectrum Disorder (ASD) sufferers. The goal is to develop a classification model using Machine Learning (ML) algorithms that are often implemented in Brain-Computer Interfaces (BCI) technology. The tasks to be solved are as follows: pre-processing the EEG dataset signal to separate the source signal from the noise/artifact signal to produce an observation signal that is free of noise/artifact; obtaining an effective feature comparison to be used as an attribute at the classification stage; and developing a more optimal classification method for detecting people with ASD through EEG signals. The methods used are: one of the wavelet techniques, namely the Continuous Wavelet Transform (CWT), which is a technique for decomposing time-frequency signals. CWT began to be used in EEG signals because it can describe signals in great detail in the time-frequency domain. EEG signals are classified into two scenarios: classification of CWT coefficients and classification of statistical features (mean, standard deviation, skewness, and kurtosis) of CWT. The method used for classifying this research uses ML, which is currently very developed in signal processing. One of the best ML methods is Support Vector Machine (SVM). SVM is an effective super-vised learning method to separate data into different classes by finding the hyper-plane with the largest margin among the observed data. The following results were obtained: the application of CWT and SVM resulted in the best classification based on CWT coefficients and obtained an accuracy of 95% higher than the statistical feature-based classification of CWT, which obtained an accuracy of 65%. Conclusions. The scientific contributions of the results obtained are as follows: 1) EEG signal processing is performed in ASD children using feature extraction with CWT and classification with SVM; 2) the combination of these signal classification methods can improve system performance in ASD EEG signal classification; 3) the implementation of this research can later assist in detecting ASD EEG signals based on brain wave characteristics.