Use of Some Relevant Parameters for Primary Prediction of Brain Activity in Idiopathic Tinnitus Based on a Machine Learning Application

Abstract

Introduction: Tinnitus is one of the most common complaints, distressing about 15–24% of the adult population. Because of its pathophysiology heterogeneity, no curable treatment has been attained yet. Even though a neuromodulation management technique based on the tinnitus network model is currently being developed, it has not yet worked because the most involved brain areas still remain unpredictable from the patient’s individual clinical and functional profile. A remarkable correlation between tinnitus network activity and the subjective measures of tinnitus like perceived loudness and annoyance and functional handicap is well established. Therefore, this study aimed to develop software for predicting the involved brain areas in the tinnitus network based on the subjective characteristics and clinical profile of patients using a supervised machine-learning method. Methods: The involved brain areas of 30 tinnitus patients ranging from 6 to 80 months in duration were recognized by using QEEG and sLORETA software. There was a correlation between subjective information and those areas of activities in all rhythms by which we wrote our software. Results: For verification and validation of the software, we compared and analyzed the results with SPSS data and the receiver operating characteristic (ROC) curves. Conclusions: The findings of this study confirmed the effectiveness of the software in predicting the brain activity in tinnitus subjects; however, some other important parameters can be added to the model to strengthen its reliability and feasibility in clinical use.