Abstract

SALIDO-RUIZ, Ricardo A. et al. Neuromarkers based on EEG Statistics in Time and Frequency Domains to Detect Tinnitus. Rev. mex. ing. bioméd [online]. 2023, vol.44, n.3, e1355.  Epub Apr 12, 2024. ISSN 2395-9126.  https://doi.org/10.17488/rmib.44.3.1.

Tinnitus detection and characterization requires a carefully elaborated diagnosis mainly owing to its heterogeneity nature. The present investigation aims to find features in Electroencephalographic (EEG) signals from time and frequency domain analysis that could distinguish between healthy and tinnitus sufferers with different levels of hearing loss. For this purpose, 24 volunteers were recruited and equally divided into four groups: 1) controls, 2) slow tinnitus, 3) middle tinnitus and 4) high tinnitus. EEG signals were registered in two states, with eyes closed and opened for 60 seconds. EEG analysis was focused on two bandwidths: delta and alpha band. For time domain, the EEG features estimated were mean, standard deviation, kurtosis, maximum peak, skewness and shape. For frequency domain, the EEG features obtained were mean, skewness, power spectral density. Normality of EEG data was evaluated by the Lilliefors test, and as a result, the nonparametric technique Kruskal-Wallis H statistic to test significance was applied. Results show that EEG features are more differentiable between tinnitus sufferers and controls in frequency domain than in time domain. EEG features from tinnitus patients with high HL are significantly different from the rest of the groups in alpha frequency band activity when shape and skewness are computed.

Keywords : clinical diagnosis; frequency features; hearing loss; neuromarkers; tinnitus; time features.

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