R S Moharari, E Rahimi, A Najafi, P Khashayar, M R Khajavi, A P Meysamie
Teaching critical appraisal and statistics in anesthesia journal club
QJM : monthly journal of the Association of Physicians
primary objective research examine acrophobia widely prevalent highly severe phobia characterized overwhelming dread heights substantial impact significant proportion individuals worldwide objective study develop realtime precise instrument evaluating levels acrophobia utilizing electroencephalogram eeg signals eeg data gathered sample 18 individuals diagnosed acrophobia subsequently range classifiers namely support vector classifier svc knearest neighbors knn random forest rf decision tree dt adaboost linear discriminant analysis lda convolutional neural network cnn artificial neural network ann employed analysis methodologies encompass machine learning ml deep learning dl techniques convolutional neural network cnn artificial neural network ann models demonstrated notable efficacy convolutional neural network cnn model demonstrated training accuracy 96 testing accuracy 99 whereas artificial neural network ann model attained training accuracy 96 testing accuracy 97 findings study highlight effectiveness proposed methodology accurately categorizing realtime degrees acrophobia using eeg data investigation using correlation matrices level acrophobia showed substantial eeg frequency band connections beta gamma mean values correlated strongly suggesting cognitive arousal acrophobic involvement could synchronize activity beta gamma activity correlated strongly acrophobia especially higher levels results underscore promise innovative approach dependable sophisticated method evaluating acrophobia methodology potential make substantial contribution toward comprehension assessment acrophobia hence facilitating development individualized efficacious therapeutic interventions