Tinnitus and distress: an electroencephalography classification study

Abstract

There exist no objective markers for tinnitus or tinnitus disorders, which complicates diagnosis and treatments. The combination of EEG with sophisticated classification procedures may reveal biomarkers that can identify tinnitus and accurately differentiate different levels of distress experienced by patients. EEG recordings were obtained from 129 tinnitus patients and 142 healthy controls. Linear support vector machines were used to develop two classifiers: the first differentiated tinnitus patients from controls, while the second differentiated tinnitus patients with low and high distress levels. The classifier for healthy controls and tinnitus patients performed with an average accuracy of 96% and 94% for the training and test sets, respectively. For the distress classifier, these average accuracies were 89% and 84%. Minimal overlap was observed between the features of the two classifiers. EEG-derived features made it possible to accurately differentiate healthy controls and tinnitus patients as well as low and high distress tinnitus patients. The minimal overlap between the features of the two classifiers indicates that the source of distress in tinnitus, which could also be involved in distress related to other conditions, stems from different neuronal mechanisms compared to those causing the tinnitus pathology itself.

EEG signals from tinnitus sufferers at identifying their sound tinnitus

“…The variable used to estimate the optimal sample size was EEG Power Spectral Density (PSD) on gamma oscillations (30–90 Hz) at resting state with open eyes, on channels T7 and T8 because the temporal lobes have been reported as a relevant brain cortex areas to distinguish tinnitus from controls in previous research [2] . PSD on gamma has been reported as an accurate biomarker for aberrant gamma oscillations in central nervous system diseases like hearing impairment [3] and tinnitus [4] .…”

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

“…See the supplementary section for a detailed explanation of how each feature set was computed. The (standard) frequency April 8, 2024 4/25 bands utilized for computing features encompass: Delta (0.5-4.5 Hz), Theta (4.5-8.5 Hz), Alpha (8.5-13.5 Hz), Beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and Gamma (30-80 Hz) [44].…”

“…In past studies, various machine learning approaches were applied to differentiate the tinnitus population from healthy controls using resting state EEG data with high accuracy [17][18][19][20]. However, to this day, machine learning studies concerning active manipulation of tinnitus have not been carried-out within the tinnitus population.…”