Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. METHODS: This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. RESULTS: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%). CONCLUSIONS: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Music and movement therapy improves quality of life and attention and associated electroencephalogram changes in patients with attention-deficit/hyperactivity disorder.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurobehavioral disorder. Treatments for ADHD include pharmacological and nonpharmacological therapy. However, pharmacological treatments have side effects such as poor appetite, sleep disturbance, and headache. Moreover, nonpharmacological treatments are not effective in ameliorating core symptoms and are time-consuming. Hence, developing an alternative and effective treatment without (or with fewer) side effects is crucial. Music therapy has long been used to treat numerous neurological diseases. Although listening to music is beneficial for mood and cognitive functions in patients with ADHD, research on the effects of music and movement therapy in children with ADHD is lacking. METHODS: The present study investigated the effects of an 8-week music and movement intervention in 13 children with ADHD. The Pediatric Quality of Life Inventory (PedsQL) was used to evaluate changes in participants' quality of life. Conners' Kiddie Continuous Performance Test (K-CPT 2) and the Swanson, Nolan, and Pelham rating scale (SNAP-IV) were used to assess core symptoms. Electroencephalogram (EEG) recordings were analyzed to determine neurophysiological changes. RESULTS: The results revealed that the participants' quality of life increased significantly after the 8-week intervention. Furthermore, the participants' hit reaction times in the block 1 and block 2 tests of K-CPT 2 decreased significantly after the intervention. EEG analysis demonstrated an increase in alpha power and Higuchi's fractal dimension and a decrease in delta power in certain EEG channels. CONCLUSION: Our music and movement intervention is a potential alternative and effective tool for ADHD treatment and it can significantly improve patients' quality of life and attention.

Neurological manifestations of SARS-CoV-2 infection in children in Taiwan: A cross-section, multicenter study.

BACKGROUND: The SARS-CoV-2 virus has been a global public health threat since December 2019. This study aims to investigate the neurological characteristics and risk factors of coronavirus disease 2019 (COVID-19) in Taiwanese children, using data from a collaborative registry. METHODS: A retrospective, cross-sectional, multi-center study was done using an online network of pediatric neurological COVID-19 cohort collaborative registry. RESULTS: A total of 11160 COVID-19-associated emergency department (ED) visits and 1079 hospitalizations were analyzed. Seizures were the most common specific neurological symptom, while encephalitis and acute disseminated encephalomyelitis (ADEM) was the most prevalent severe involvement. In ED patients with neurological manifestations, severe neurological diagnosis was associated with visual hallucination, seizure with/without fever, behavior change, decreased GCS, myoclonic jerk, decreased activity/fatigue, and lethargy. In hospitalized patients with neurological manifestations, severe neurological diagnosis was associated with behavior change, visual hallucination, decreased GCS, seizure with/without fever, myoclonic jerk, fatigue, and hypoglycemia at admission. Encephalitis/ADEM was the only risk factor for poor neurological outcomes at discharge in hospitalized patients. CONCLUSION: Neurological complications are common in pediatric COVID-19. Visual hallucination, seizure, behavior change, myoclonic jerk, decreased GCS, and hypoglycemia at admission are the most important warning signs of severe neurological involvement such as encephalitis/ADEM.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram.

Although the remission of self-limited epilepsy with centrotemporal spikes (SeLECTS) usually occurs by adolescence, deficits in cognition and behavior are not uncommon. Several functional magnetic resonance imaging (fMRI) studies have revealed connectivity disturbances in patients with SeLECTS associated with cognitive impairment. However, the disadvantages of fMRI are expensive, time-consuming, and motion sensitive. In the current study, we used a partial directed coherence (PDC) method to analyze electroencephalogram (EEG) for exploring brain connectivity in patients with SeLECTS. This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in the controls than in the patients with SeLECTS, whereas the inflow connectivity in the MIF_L area 4 was significantly higher in the patients with SeLECTS than in the controls. Our proposed approach of combining EEG with PDC provides a convenient and useful tool for investigating functional connectivity in patients with SeLECTS. This approach is time-saving and inexpensive compared with fMRI, but it achieves similar results to fMRI.

Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. METHODS: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients-31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants' movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. RESULTS: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. CONCLUSION: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD.

Clinical and Genetic Features of Dravet Syndrome: A Prime Example of the Role of Precision Medicine in Genetic Epilepsy.

Dravet syndrome (DS), also known as severe myoclonic epilepsy of infancy, is a rare and drug-resistant form of developmental and epileptic encephalopathies, which is both debilitating and challenging to manage, typically arising during the first year of life, with seizures often triggered by fever, infections, or vaccinations. It is characterized by frequent and prolonged seizures, developmental delays, and various other neurological and behavioral impairments. Most cases result from pathogenic mutations in the sodium voltage-gated channel alpha subunit 1 (SCN1A) gene, which encodes a critical voltage-gated sodium channel subunit involved in neuronal excitability. Precision medicine offers significant potential for improving DS diagnosis and treatment. Early genetic testing enables timely and accurate diagnosis. Advances in our understanding of DS's underlying genetic mechanisms and neurobiology have enabled the development of targeted therapies, such as gene therapy, offering more effective and less invasive treatment options for patients with DS. Targeted and gene therapies provide hope for more effective and personalized treatments. However, research into novel approaches remains in its early stages, and their clinical application remains to be seen. This review addresses the current understanding of clinical DS features, genetic involvement in DS development, and outcomes of novel DS therapies.

Alopecia and colon ulcers following azathioprine use in a patient with myasthenia gravis: A case report.

RATIONALE: Azathioprine is a purine analog (PA) used to treat myasthenia gravis (MG). However, some patients are sensitive to azathioprine and develop severe side effects, such as leukopenia, alopecia, and diarrhea soon after using the medication. Pharmacogenetics plays a crucial role in such intolerance. PATIENT CONCERNS: A 16-year-old woman with MG developed hair loss, pancytopenia, bloody diarrhea, and fever shortly after azathioprine treatment. DIAGNOSIS: Pharmacogenetic analysis revealed compound heterozygosity of the nudix hydrolase 15 (NUDT15) gene, which led to suppressed NUDT15 function. Colonoscopy revealed large ulcers with polypoid lesions in the terminal ileum, cecum, ascending colon, and rectum. These are the characteristics of inflammatory bowel disease (IBD). INTERVENTIONS: Sanger sequencing of NUDT15 gene and colonoscopy for bloody stool evaluation. OUTCOMES: The patient recovered completely from this acute episode after discontinuation of azathioprine treatment. Her hemogram turned back to normal range. There was also no blood in stool during follow-up. LESSONS: Pharmacogenetic effects should be considered when prescribing PA medication. The possibility of secondary or concomitant autoimmune diseases must always be considered in patients with MG.

Prediction of seizure recurrence using electroencephalogram analysis with multiscale deep neural networks before withdrawal of antiepileptic drugs.

BACKGROUND: The decision to continue or discontinue antiepileptic drug (AED) treatment in patients who are seizure free for a prolonged time is critical. Studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, applicable biomarkers to guide the withdrawal of AEDs are lacking. METHODS: In this study, we used EEG analysis based on multiscale deep neural networks (MSDNN) to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 60 patients with epilepsy were divided into two groups (30 in the recurrence group and 30 in the non-recurrence group). All patients were seizure free for at least 2 years. Before AED withdrawal, an EEG was performed for each patient, which showed no epileptiform discharges. These EEG recordings were classified using MSDNN. RESULTS: We found that the performance indices of classification between recurrence and non-recurrence groups had a mean sensitivity, mean specificity, mean accuracy, and mean area under the receiver operating characteristic curve of 74.23%, 75.83%, 74.66%, and 82.66%, respectively. CONCLUSION: Our proposed method is a promising tool to help physicians to predict seizure recurrence after AED withdrawal among seizure-free patients.

Early and Objective Evaluation of the Therapeutic Effects of ADHD Medication through Movement Analysis Using Video Recording Pixel Subtraction.

Attention-deficit/hyperactivity disorder (ADHD) affects approximately 5−7% of school-age children. ADHD is usually marked by an ongoing pattern of inattention or hyperactivity−impulsivity, leading to functioning or developmental problems. A common ADHD assessment tool is the Swanson, Nolan, and Pelham (SNAP) questionnaire. However, such scales provide only a subjective perspective, and most of them are used to evaluate therapeutic effects at least 3−12 months after medication initiation. Therefore, we employed an objective assessment method to provide more accurate evaluations of therapeutic effects in 25 children with ADHD (23 boys and 2 girls). To evaluate the participants' improvement and treatment's effectiveness, the pixel subtraction technique was used in video analysis. We compared the efficacy of 1-month Ritalin or Concerta treatment by evaluating the movement in each video within 3 h of medication administration. The movement value was defined as the result of a calculation when using the pixel subtraction technique. Based on behavior observation and SNAP scores, both parent- and teacher-reported scores decreased after 1 month of medication (reduction rates: 19.61% and 16.38%, respectively). Specifically, the parent-reported hyperactivity subscale and teacher-reported oppositional subscale decreased more significantly. By contrast, the reduction rate was 39.27%, as evaluated using the average movement value (AMV). Considering symptomatic improvement as a >25% reduction in scores, the result revealed that the AMV decreased in 18 patients (72%) compared with only 44% and 56% of patients based on parent- and teacher-reported hyperactivity subscale scores. In conclusion, the pixel subtraction method can serve as an objective and reliable evaluation of the therapeutic effects of ADHD medication in the early stage.

Evaluating Therapeutic Effects of ADHD Medication Objectively by Movement Quantification with a Video-Based Skeleton Analysis.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in children. Several scales are available to evaluate ADHD therapeutic effects, including the Swanson, Nolan, and Pelham (SNAP) questionnaire, the Vanderbilt ADHD Diagnostic Rating Scale, and the visual analog scale. However, these scales are subjective. In the present study, we proposed an objective and automatic approach for evaluating the therapeutic effects of medication in patients with (ADHD). The approach involved using movement quantification of patients' skeletons detected automatically with OpenPose in outpatient videos. Eleven skeleton parameter series were calculated from the detected skeleton sequence, and the corresponding 33 features were extracted using autocorrelation and variance analysis. This study enrolled 25 patients with ADHD. The outpatient videos were recorded before and after medication treatment. Statistical analysis indicated that four features corresponding to the first autocorrelation coefficients of the original series of four skeleton parameters and 11 features each corresponding to the first autocorrelation coefficients of the differenced series and the averaged variances of the original series of 11 skeleton parameters significantly decreased after the use of methylphenidate, an ADHD medication. The results revealed that the proposed approach can support physicians as an objective and automatic tool for evaluating the therapeutic effects of medication on patients with ADHD.

Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis.

The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients.

Increased Temporal Lobe Beta Activity in Boys With Attention-Deficit Hyperactivity Disorder by LORETA Analysis.

Aim: Attention-deficit hyperactivity disorder (ADHD) is a common childhood neuropsychiatric disorder that affects 6.1 million US children. The mechanism of ADHD is currently unclear. Differences in ADHD presentations between boys and girls are well-established. In the present study, we used quantitative electroencephalography (EEG) to investigate the brain area and EEG bands of boys with ADHD. Methods: This study enrolled 40 boys with ADHD and 40 age-matched controls without ADHD. Low-resolution electromagnetic tomography (LORETA) and instantaneous frequency were used to analyze EEG data to reveal the mechanisms underlying ADHD in boys. Results: We found that the instantaneous frequencies in the T3 and T4 EEG channels in boys with ADHD were significantly higher than those in the controls. The beta band showed significant difference in current density between the ADHD and control groups. In the entire brain area, the bilateral inferior and middle temporal gyrus exhibited the most significant difference between the ADHD and control groups in the EEG beta band. Connectivity analysis revealed an increase in connectivity between the left middle frontal gyrus and fusiform gyrus of the temporal lobe in boys with ADHD. Conclusions: LORETA is a promising tool for analyzing EEG signals and can be used to investigate the mechanism of ADHD. Our results reveal that the inferior temporal gyrus, middle temporal gyrus, and fusiform gyrus of the temporal lobe are potentially involved in the pathogenesis of ADHD in boys. In comparison with other imaging methods, such as magnetic resonance imaging, EEG is easy to perform, fast, and low cost. Our study presents a new approach for investigating the pathogenesis of ADHD in boys.

EEG autoregressive modeling analysis: A diagnostic tool for patients with epilepsy without epileptiform discharges.

OBJECTIVE: Numerous types of nonepileptic paroxysmal events, such as syncopes and psychogenic nonepileptic seizures, may imitate epileptic seizures and lead to diagnostic difficulty. Such misdiagnoses may lead to inappropriate treatment in patients that can considerably affect their lives. Electroencephalogram (EEG) is a commonly used tool in assisting diagnosis of epilepsy. Although the appearance of epileptiform discharges (EDs) in EEG recordings is specific for epilepsy diagnosis, only 25%-56% of patients with epilepsy show EDs in their first EEG examination. METHODS: In this study, we developed an autoregressive (AR) model prediction error-based EEG classification method to distinguish EEG signals between controls and patients with epilepsy without EDs. Twenty-three patients with generalized epilepsy without EDs in their EEG recordings and 23 age-matched controls were enrolled. Their EEG recordings were classified using AR model prediction error-based EEG features. RESULTS: Among different classification methods, XGBoost achieved the highest performance in terms of accuracy and true positive rate. The results showed that the accuracy, area under the curve, true positive rate, and true negative rate were 85.17%, 87.54%, 89.98%, and 81.81%, respectively. CONCLUSIONS: Our proposed method can help neurologists in the early diagnosis of epilepsy in patients without EDs and might help in differentiating between nonepileptic paroxysmal events and epilepsy. SIGNIFICANCE: EEG AR model prediction errors could be used as an alternative diagnostic marker of epilepsy.

Quantitative electroencephalogram analysis of frontal cortex functional changes in patients with migraine.

The functional abnormality of brain areas accounting for the migraine remains to be elucidated. Most related studies have used functional magnetic resonance imaging to investigate brain areas involved in migraine. However, the results are heterogeneous. In this study, we used a convenient tool to explore the brain regions involved in migraine. In this study, 40 children with migraine and 40 sex- and age-matched health controls were enrolled, and electroencephalogram was used to explore the functional abnormal areas of migraine through electroencephalogram bands and low-resolution electromagnetic tomography analysis. The results revealed that spectrum edge frequency 50 in all electroencephalogram channels in patients with migraine were lower than those in controls. Significant differences were discovered over frontal areas. In addition, significantly higher current density over the frontopolar prefrontal cortex and orbitofrontal cortex and higher connectivity over the left prefrontal cortex were observed in patients with migraine. We suggest that functional disturbance of the prefrontal cortex may play a potential role in children with migraine, and that low-resolution electromagnetic tomography is a reliable and convenient tool for studying the functional disturbance of migraine.

Delay Maturation in Occipital Lobe in Girls With Inattention Subtype of Attention-Deficit Hyperactivity Disorder.

Attention-deficit hyperactivity disorder (ADHD) is a common childhood neuropsychiatric disorder. Differences in the presentations of ADHD between boys and girls have been well established. Three subtypes of ADHD exist. In addition to sex difference, different mechanisms may underlie different subtypes. The present study enrolled 30 girls with the inattentive subtype of ADHD and 30 age-matched controls. Low-resolution electromagnetic tomography (LORETA) and instantaneous frequency were used to analyze electroencephalography (EEG) for investigating the brain area and EEG bands involved in girls with inattentive ADHD. We found that the instantaneous frequencies in all EEG channels in girls with ADHD were lower than those in controls. Alpha 2 was the only EEG band that showed significant difference in current density between the ADHD and control groups (P = .0014). In the entire brain area, the posterior cingulate cortex, cingulate gyrus, and precuneus demonstrated the most significant difference between the ADHD and control groups. Our results suggest that brain maturation delay in the posterior areas might result in the inattention subtype of ADHD. In addition, posterior cingulate cortex, cingulate gyrus, and precuneus may play a critical role in the pathogenesis of ADHD. Our study provides a new approach method and possible mechanism of girls with inattentive subtype ADHD.

Alternative Diagnosis of Epilepsy in Children Without Epileptiform Discharges Using Deep Convolutional Neural Networks.

Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients' lives considerably. Electroencephalography is commonly used for diagnosing epilepsy. Although on electroencephalograms (EEGs), epileptiform discharges (ED) specifically indicate epilepsy, only approximately 50% of patients with epilepsy have ED in their first EEG. In this study, we developed a deep convolutional neural network (ConvNet)-based classifier to distinguish EEG between patients with epilepsy without ED and controls. Overall, 25 patients with epilepsy without ED in their EEGs and 25 age-matched patients with Tourette syndrome or syncope were enrolled. Their EEGs were classified using the deep ConvNet. When the EEG data without overlapping were used, the accuracy, sensitivity, and specificity were 65.00%, 48.00%, and 82.00%, respectively. The performance measures improved when the input EEG data were augmented through overlapping. With 95% EEG data overlapping, the accuracy, sensitivity, and specificity increased to 80.00%, 70.00%, and 90.00%, respectively. The proposed method could be regarded as a pilot study to demonstrate a proof of concept of a potential diagnostic value of deep ConvNet in patients with epilepsy without ED. Further studies are needed to assist neurologists in distinguishing nonepileptic paroxysmal events from epilepsy.

A New Method of Diagnosing Attention-Deficit Hyperactivity Disorder in Male Patients by Quantitative EEG Analysis.

Attention-deficit hyperactivity disorder (ADHD) is one of the most common neuropsychiatric disorders of childhood. Diagnosis of ADHD is based on core symptoms and checklists. However, these are both subjective, which can lead to the problems of overdiagnosis and underdiagnosis. Elevated theta/beta ratio (TBR) of EEG band has been approved by the US Food and Drug Administration as a tool to assist in the diagnosis of ADHD. However, several recent studies have demonstrated that there are no significant differences in TBR between people with and without ADHD. In this study, we attempted to develop a new method for differentiating between male with and without ADHD by analyzing EEG features. Thirty boys with ADHD combined type (aged 8 years 5 months ± 1 year 11 months) and 30 age-matched controls (aged 8 years 5 months ± 1 year 8 months) were enrolled in this study. A classification analysis-based approach comprising training and classification phases was developed for classifying each subject's EEG features as ADHD or non-ADHD. Eight crucial feature descriptors were selected and ranked based on the t test. Compared with TBR in our study, the developed method had a higher area under the curve (87.78%), sensitivity (80.0%), and specificity (80.0%). Our method is more precise than using TBR in the diagnosis of ADHD. This newly developed method is a useful tool in identifying patients with ADHD and might reduce the possibility of overdiagnosis and underdiagnosis.

Novel method using Hjorth mobility analysis for diagnosing attention-deficit hyperactivity disorder in girls.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a common childhood neuropsychiatric disorder. Diagnosis of ADHD is based on core symptoms or checklists; however, practitioner subjectivity inevitably results in instances of over- or under-diagnosis. Although an elevated theta/beta ratio (TBR) of the electroencephalography (EEG) band has been approved by the Food and Drug Administration as a factor that may be used in diagnosis of ADHD, several studies have reported no significant differences between the TBR of patients with ADHD and controls. PURPOSE: In this study, a method was developed based on Hjorth Mobility (M) analysis of EEG to compare patients with ADHD and controls. METHODS: Differences in the presentations of ADHD between boys and girls are well established; therefore, separate investigations are required. The present study enrolled 30 girls with ADHD and 30 age-matched controls. RESULTS: The results revealed that the control group had significantly higher Hjorth M values in most brain areas in EEG readings compared with the values for the ADHD group. Compared with TBR, our method revealed a greater number of more significant differences between the girls in the ADHD group and the controls. Moreover, our method can produce the higher average sensitivity (0.796), average specificity (0.796), average accuracy (0.792), and average area under the curve of receiver operating characteristic curve (AUC) value (0.885). Therefore, compared with TBR, Hjorth M possessed the better potential for differentiating between girls with ADHD and controls. CONCLUSION: The proposed method was more accurate than the TBR in diagnosing ADHD. Therefore, Hjorth M may be a promising tool for differentiating between children with ADHD and controls.

Entropy-Based Quantitative Electroencephalogram Analysis for Diagnosing Attention-Deficit Hyperactivity Disorder in Girls.

Diagnosis of attention-deficit hyperactivity disorder (ADHD) is currently based on core symptoms or checklists; however, the inevitability of practitioner subjectivity leads to over- and underdiagnosis. Although the Federal Drug Administration has approved an elevated theta/beta ratio (TBR) of the electroencephalogram (EEG) band as a tool for assisting ADHD diagnosis, several studies have reported no significant differences of the TBR between ADHD and control subjects. This study detailed the development of a method based on approximate entropy (ApEn) analysis of EEG to compare ADHD and control groups. Differences between ADHD presentation in boys and girls indicate the necessity of separate investigations. This study enrolled 30 girls with ADHD and 30 age-matched controls. The results revealed significantly higher ApEn values in most brain areas in the control group than in the ADHD group. Compared with TBR-related feature descriptors, ApEn-related feature descriptors can produce the higher average true positive rate (0.846), average true negative rate (0.814), average accuracy (0.817), and average area under the receiver operating characteristic curve value (0.862). Therefore, compared with TBR, ApEn possessed the better potential for differentiating between girls with ADHD and controls.