Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls

Parashar, Anshu; Kalra, Nidhi; Singh, Jaskirat; Goyal, Raman Kumar

doi:10.32604/iasc.2021.017478

Cited by 22 publications

(16 citation statements)

References 20 publications

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“…Our approach has achieved better performance than the existing approaches in Chen et al (2019) , Altınkaynak et al (2020) , Ekhlasi et al (2021) , Kim et al (2021) , Parashar et al (2021) , Maniruzzaman et al (2022) , and Alim and Imtiaz (2023) . The approaches in Chen et al (2019) , Altınkaynak et al (2020) , and Kim et al (2021) have performed experiments on different datasets, while the approaches in Ekhlasi et al (2021) , Parashar et al (2021) , Maniruzzaman et al (2022) , and Alim and Imtiaz (2023) have performed experiments on the same dataset as ours. Chen et al (2019) performed four distinct methods: relative spectral power, spectral power ratio, complexity analyses, and bicoherence for resting-state EEG feature extraction.…”

Section: Resultsmentioning

confidence: 83%

Gabor filter-based statistical features for ADHD detection

Sathiya,

Rao,

Kumar

2024

Front. Hum. Neurosci.

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Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.

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Section: Resultsmentioning

confidence: 83%

Gabor filter-based statistical features for ADHD detection

Sathiya,

Rao,

Kumar

2024

Front. Hum. Neurosci.

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“…Our study aimed was to automatically classify children as ADHD or healthy by the morphological and time-domain features of EEG signals in an ML-based algorithm. Most of the studies utilized (extracted features) morphological, time-domain, frequency domain, and non-linear features [5,6,26,28,29,51] from EEG signals to classify children as ADHD or healthy. Even though these studies produced a higher performance score for distinguishing children with ADHD from healthy children, scientists and researchers attempted to develop a model for identifying reliable features in EEG signals to diagnose children with ADHD as early as possible.…”

Section: Discussionmentioning

confidence: 99%

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Maniruzzaman¹,

Shin²,

Hasan³

et al. 2022

Computers, Materials &Amp; Continua

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Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7-12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The selected potential features were used in four ML-based algorithms, including support vector machine (SVM), k-nearest neighbors, multilayer perceptron (MLP), and logistic regression, to classify ADHD and healthy children. The overall prevalence of boys and girls with ADHD was 48.9% and 56.5%, respectively. The average age of children with ADHD was 9.6±1.8 years. Our results illustrated that the combination of LASSO with SVM classifier achieved the highest accuracy of 94.2%, sensitivity of 93.3%, F1-score of 91.9%, and AUC of 0.964. Our results also illustrated that MLP was the second-best ML-based classifier, which gave 93.4% accuracy, 91.7% sensitivity, 91.1% F1-score, and 0.960 AUC. The findings indicated that the combination of the LASSO-based feature selection method and SVM classifier can be a useful tool for selecting reliable/potential features and classifying ADHD and healthy children. Our proposed ML-based algorithms could be useful for the early diagnosis of children with ADHD.

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“…Altınkaynak et al [4] discriminated children with ADHD and healthy controls using MLP, NB, SVM, k-NN, AdaBoost (AB), LR, and RF and obtained an accuracy rate of 91.3% by MLP. Parashar et al [13] also studied sixty children with ADHD and sixty healthy control children. They adopted three classifiers (AB, RF, and SVM) for classification and obtained an 84.0% accuracy rate with AB.…”

Section: F Comparison Of Our Proposed Work Against Previous Studiesmentioning

confidence: 99%

“…, ML-based approaches have been widely used in the field of medical imaging [46], time series [20], [21], [55], [56], and bioinformatics [57]. Like other domains, ML-based approaches were also widely used to discriminate children with ADHD from healthy control [4], [10], [13], [24], [25], [47], [49], [52], [58]- [60]. For example, Muller et al [61] proposed SVM-based children with ADHD detection system.…”

Section: Introductionmentioning

confidence: 99%

“…Ahmadlou and Adeli [63] proposed an ADHD detection approach with the integration of non-linear features, wavelets, signal-based processing techniques, and neural networks and got a classification accuracy of 95.6%. Parashar et al [13] also proposed an automated system for detecting children with ADHD. Helgadóttir et al [60] proposed a classification approach for the detection of children with ADHD using EEG signals.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls

Cited by 22 publications

References 20 publications

Gabor filter-based statistical features for ADHD detection

Gabor filter-based statistical features for ADHD detection

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

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

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