Steven Fussner, Aidan Boyne, Albert Han, Lauren A Nakhleh, Zulfi Haneef
Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images
Sensors (Basel, Switzerland)
research investigates application machine learning improve diagnosis tinnitus using highfrequency audiometry data logistic regression lr model developed alongside artificial neural network ann various baseline classifiers identify effective approach classifying tinnitus presence methodology encompassed data preprocessing feature extraction focused point detection rigorous model evaluation performance metrics including accuracy area roc curve auc precision recall f1 scores main findings reveal lr model supported ann significantly outperformed machine learning models achieving accuracy 9406 auc 9706 high precision recall scores results demonstrate efficacy lr model ann accurately diagnosing tinnitus surpassing traditional diagnostic methods rely subjective assessments implications research substantial clinical audiology suggesting machine learning particularly advanced models like anns provide objective quantifiable tool tinnitus diagnosis especially utilizing highfrequency audiometry data typically assessed standard hearing tests study underscores potential machine learning facilitate earlier accurate tinnitus detection could lead improved patient outcomes future work aim expand dataset diversity explore broader range algorithms conduct clinical trials validate models practical utility research highlights transformative potential machine learning including lr model ann audiology paving way advancements diagnosis treatment tinnitus