Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

Abstract

A TTENTION deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems.To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.

Gabor filter-based statistical features for ADHD detection

“…To classify the EEG segment into ADHD and HC, the feature vector is fed into classifiers. In our approach, we have used two classifiers; namely support vector machine (SVM) ( Maniruzzaman et al, 2023 ) and k-nearest neighbors (k-NN) ( Altınkaynak et al, 2020 ).…”

“…Feature selection (FS) is important for improving the performance of predictive models by eliminating redundant elements in a dataset, thereby maintaining only the most important features. In our study, we explored the t -test ( Maniruzzaman et al, 2023 ) and the chi-square test ( Rangarajan and Mahanand, 2014 ) to decrease the length of the feature vector and improve the accuracy (Acc) of classification.…”

“…However, some of these approaches, such as MEG, are radioactive, bulky, and costly ( Khare and Acharya, 2023 ). On the other hand, EEG signals are portable and cost-effective solutions for ADHD detection ( Maniruzzaman et al, 2023 ), and they have also been used in various applications ( Kumar et al, 2015 ; Khare and Bajaj, 2020 ).…”

“…Over the last decade, researchers have extracted various linear, non-linear, and morphological features from time ( Yang et al, 2016 ; Khaleghi et al, 2020 ; Maniruzzaman et al, 2023 ), frequency ( Mueller et al, 2010 ; Kaur et al, 2019 ; Khaleghi et al, 2020 ), and time-frequency ( Öztoprak et al, 2017 ; Altınkaynak et al, 2020 ; Joy et al, 2022 ) domain-based methodologies. Altınkaynak et al (2020) utilized event-related potentials (ERPs), while participants engaged in an auditory oddball task, which resulted in longer P300 latency for ADHD patients and smaller P300 amplitude for healthy control (HC).…”

“… Altınkaynak et al (2020) utilized event-related potentials (ERPs), while participants engaged in an auditory oddball task, which resulted in longer P300 latency for ADHD patients and smaller P300 amplitude for healthy control (HC). Maniruzzaman et al (2023) performed a channel selection method and extracted various times, morphological, and non-linear features for the classification of ADHD and HC. The approach in Khaleghi et al (2020) extracted various morphological, non-linear, time, frequency, and time–frequency-based features; among these non-linear features (Petrosian and Katz fractal dimensions, Lyapunov exponent, approximate entropy, and Lempel-Ziv complexity) extracted from EEG, provides a good quantitative tool in the detection of ADHD.…”