首页|Data on Attention Deficit Hyperactivity Disorders Reported by Mahi Khemchandani and Colleagues [Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit ...]
Data on Attention Deficit Hyperactivity Disorders Reported by Mahi Khemchandani and Colleagues [Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Developmental Diseases and Conditions - Attention Deficit Hyperactivity Disorders is the subject of a report. According to news reporting from Mumbai, India, by NewsRx journalists, r esearch stated, “Attention deficit hyperactivity disorder (ADHD) is a neurodevel opmental disorder characterized by repeated patterns of hyperactivity, impulsivi ty, and inattention that limit daily functioning and development. Electroencepha lography (EEG) anomalies correspond to changes in brain connection and activity. ” The news correspondents obtained a quote from the research, “The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DW T) for feature extraction and machine learning (ML) algorithms to categorize ADH D and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range o f EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain’s prefrontal cortex and frontal regions collaborate in intricate netw orks, and disorders in either of them exacerbate the symptoms of ADHD. , Based o n the research that claimed the brain’s prefrontal cortex and frontal regions co llaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electr ode for identifying ADHD and in addition to monitoring accuracy on frontal/ pref rontal and other regions of brain our study also investigates the position group ings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values f or accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0. 70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0 .64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analy sis, it is observed that the most accurate results included all electrodes.”
MumbaiIndiaAsiaADHDAttention Def icit Hyperactivity DisordersCyborgsDevelopmental Diseases and ConditionsDi agnostics and ScreeningElectroencephalographyEmerging TechnologiesHealth a nd MedicineMachine LearningMental HealthMental Health Diseases and Conditi ons
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