Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Ekhlasi, Ali; Nasrabadi, Ali Motie; Mohammadi, Mohammad-Reza

doi:10.18502/fbt.v8i2.6515

Cited by 18 publications

(12 citation statements)

References 27 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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“…Ekhlasi et al (Ekhlasi et al 2021) used same ADHD dataset in our model. 5) suggest that our model achieved robust results and that our method is effective.…”

Section: Discussionmentioning

confidence: 99%

EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals

et al. 2022

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Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.

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“…They adopted three classifiers (AB, RF, and SVM) for classification and obtained an 84.0% accuracy rate with AB. Ekhlasi et al [59] proposed a system that can be easily classified into children with ADHD and healthy controls. They applied genetic algorithms for feature selection and NN for classification.…”

Section: F Comparison Of Our Proposed Work Against Previous Studiesmentioning

confidence: 99%

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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Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Cited by 18 publications

References 27 publications

Gabor filter-based statistical features for ADHD detection

Gabor filter-based statistical features for ADHD detection

EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals

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

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