Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related...

Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies on subjective assessments. However, its neuropathological features can be objectively quantified using electroencephalography (EEG). Despite the existing research, the pathophysiology of tinnitus remains unclear. The objective of this study was to gain a deeper comprehension of the neural mechanisms underlying tinnitus through the comparison of cognitive event-related potentials in patients with tinnitus and healthy controls (HCs). Furthermore, we explored the potential of EEG-derived features as biomarkers for tinnitus using machine learning techniques.

Forty-eight participants (24 patients with tinnitus and 24 HCs) underwent comprehensive audiological assessments and EEG recordings. We extracted N2 and P3 components of the midline electrodes using an auditory oddball paradigm, to explore the relationship between tinnitus and cognitive function. In addition, the current source density for N2- and P3-related regions of interest was computed. A linear support vector machine classifier was used to distinguish patients with tinnitus from HCs.

The P3 peak amplitudes were significantly diminished in patients with tinnitus at the AFz, Fz, Cz, and Pz electrodes, whereas the N2 peak latencies were significantly delayed at Cz electrode. Source analysis revealed notably reduced N2 activities in bilateral fusiform gyrus, bilateral cuneus, bilateral temporal gyrus, and bilateral insula of patients with tinnitus. Correlation analysis revealed significant associations between the Hospital Anxiety and Depression Scale-Depression scores and N2 source activities at left insula, right insula, and left inferior temporal gyrus. The best classification performance showed a validation accuracy of 85.42%, validation sensitivity of 87.50%, and validation specificity of 83.33% in distinguishing between patients with tinnitus and HCs by using a total of 18 features in both sensor- and source-level.

This study demonstrated that patients with tinnitus exhibited significantly altered neural processing during the cognitive-related oddball paradigm, including lower P3 amplitudes, delayed N2 latency, and reduced source activities in specific brain regions in cognitive-related oddball paradigm. The correlations between N2 source activities and Hospital Anxiety and Depression Scale-Depression scores suggest a potential link between the physiological symptoms of tinnitus and their neural impact on patients with tinnitus. Such findings underscore the potential diagnostic relevance of N2- and P3-related features in tinnitus, while also highlighting the interplay between the temporal lobe and occipital lobe in tinnitus. Furthermore, the application of machine learning techniques has shown reliable results in distinguishing tinnitus patients from HCs, reinforcing the viability of N2 and P3 features as biomarkers for tinnitus.