Evaluation of feature extraction techniques on event-related potentials for detection of attention-deficit/hyperactivity disorder

Castro-Cabrera, Paola Alexandra; García, Jorge Andrés Gómez; Restrepo, Francia; Moscoso, O.; Castellanos-Domínguez, G.

doi:10.1109/iembs.2010.5626862

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…• Biomedical signals. Large part of the studies have focused on electroencephalogram (EEG) [42,30,32,41,43,31] and event-related potentials (ERP) [26,12]. More innovative (and much more expensive) techniques such as magnetoencephalography (MEG) have been also applied [19].…”

Section: Objective Methodsmentioning

confidence: 99%

“…[35] Table 6 Separability analysis and classification performance obtained after application of previously reported actigraphy-based methods (6 most meaningful indices for each kind of method when significant results-p < 0.05-were obtained for more than 6 features, only significant results otherwise with SD reveals higher variability in the control group associated to transitions between "mid" and "high" activity levels-P(21) and P (12). These results should be carefully interpreted, since the higher dynamic range of activity in the ADHD group can lead to lower transition occurrences.…”

Section: Statisticsmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear analysis of actigraphic signals for the assessment of the attention-deficit/hyperactivity disorder (ADHD)

Martín-Martínez¹,

Casaseca‐de‐la‐Higuera²,

Alberola-López³

et al. 2012

Medical Engineering & Physics

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Section: Objective Methodsmentioning

confidence: 99%

Section: Statisticsmentioning

confidence: 99%

Nonlinear analysis of actigraphic signals for the assessment of the attention-deficit/hyperactivity disorder (ADHD)

Martín-Martínez¹,

Casaseca‐de‐la‐Higuera²,

Alberola-López³

et al. 2012

Medical Engineering & Physics

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“…ERP is considered as one of the most effective methods to monitor cognitive processes. Relevant researchers believed that ERP can be used to screen various pathological nerve disorders and achieve higher screening accuracy, such as ADHD and ASD (Castro-Cabrera et al, 2010). Meanwhile, some studies have shown that individuals with ASD have a poor perception of facial expression of others (Kiyoto et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Peng

Xin

et al. 2021

Front. Hum. Neurosci.

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Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy.

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“…In recent years, however, there have been attempts to extract measures that correlate with ADHD symptoms from body worn physiological sensors. Some of these objective tools are based on continuous performance tests (CPTs) [ 8 ] and electroencephalogram (EEG) [ 9 ]. EEG and CPT testing can be costly to administer, requiring expensive equipment and specialized personnel, especially in the case of EEG [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images

Muñoz-Organero

Powell

Heller

et al. 2018

Sensors

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Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved.

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Evaluation of feature extraction techniques on event-related potentials for detection of attention-deficit/hyperactivity disorder

Cited by 6 publications

References 8 publications

Nonlinear analysis of actigraphic signals for the assessment of the attention-deficit/hyperactivity disorder (ADHD)

Nonlinear analysis of actigraphic signals for the assessment of the attention-deficit/hyperactivity disorder (ADHD)

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images

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