Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals

Cura, Ozlem Karabiber; Atli, Sibel Kocaaslan; Akan, Aydın

doi:10.1016/j.bspc.2022.104512

Cited by 7 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total power of PRCs was obtained using the spectrum of signals. The spectrum of PRCs was evaluated by implementing the periodogram method, which allows for analysis of the frequency content of a signal (Iscan et al, 2011 ; Karabiber Cura et al, 2023 ). From definitions of k -th frequency ( Equation 7 ) and power power spectral desity estimation of the k -th frequency component ( Equation 8 ), the total power is defined as in Equation 9 (Iscan et al, 2011 ):

where S ( w k ) indicates the power spectral density of the signal provided by the periodogram method, X ( w k ) indicates the discrete Fourier transform of the PRC x [ n ], and S T is the total power of PRCs.…”

Section: Methodsmentioning

confidence: 99%

“…In this study for differentiation of FM imageries, the provided ITD-based EEG features have been evaluated using eight well-known machine learning algorithms, such as Decision Tree (Tzallas et al, 2009 ; Sharma et al, 2022 ), Discriminant Analysis (Hart et al, 2000 ; Chakrabarti et al, 2003 ; Lotte et al, 2018 ), Naive Bayes (Hart et al, 2000 ; Miao et al, 2017 ), Support Vector Machine (Vapnik, 1999 ; Hart et al, 2000 ; Bascil et al, 2016 ), k -Nearest Neighbor (Hart et al, 2000 ; Isler, 2009 ; Tzallas et al, 2009 ), Ensemble Learning (Sayilgan et al, 2019 , 2020 , 2021a , b , 2022 ; Degirmenci et al, 2022b , c ; Karabiber Cura et al, 2023 ), Neural Networks (Richard and Lippmann, 1991 ; Pan et al, 2012 ; Narin and Isler, 2021 ; Ozdemir et al, 2021 ; Degirmenci et al, 2022a ), and Kernel Approximation (Maji et al, 2008 ; Lei et al, 2019 ). The classifiers and corresponding algorithms that were adopted in this study are listed below in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Recent studies have performed ITD-based approaches for the analysis of biomedical signals. ITD-based feature extraction processes were conducted in various EEG-based studies for different objectives such as epilepsy detection (Martis et al, 2013 ; Degirmenci and Akan, 2020 ), and attention deficit hyperactivity disorder (ADHD) recognition (Karabiber Cura et al, 2023 ). Considering its ability to discriminate different classes, we studied to explore whether ITD promises superior use, or not, in classifying other biomedical signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EEG-based finger movement classification with intrinsic time-scale decomposition

Degirmenci,

Yuce,

Perc

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

IntroductionBrain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.MethodsIn this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.ResultsAs a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.DiscussionWhen compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EEG-based finger movement classification with intrinsic time-scale decomposition

Degirmenci,

Yuce,

Perc

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Then, the principal component analysis approach was used to reduce features that enter an SVM classifier to detect ADHD and differentiate it from healthy cases. Meanwhile, the study [ 25 ] utilized intrinsic time-scale decomposition (ITD). A number of connectivity-based features were extracted using different mixtures of extracted features from a single domain of the modes, which ITD created.…”

Section: Related Workmentioning

confidence: 99%

“…EEG is also used with assistive technology for patients with motor disabilities. EEG data can monitor alterations in brain function caused by ADHD [ 25 , 26 ]. However, complex-level structures in the complex records generated by the brains of humans are challenging to identify [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

Attallah

2024

Biomimetics

View full text Add to dashboard Cite

The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled “ADHD-AID”. The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time–frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification’s complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID’s results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.

show abstract

Gated transformer network based EEG emotion recognition

Bilgin,

Mert

2024

SIViP

View full text Add to dashboard Cite

Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals

Cited by 7 publications

References 34 publications

EEG-based finger movement classification with intrinsic time-scale decomposition

EEG-based finger movement classification with intrinsic time-scale decomposition

ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

Gated transformer network based EEG emotion recognition

Contact Info

Product

Resources

About