ArticleBrain communications2023
Tinnitus and distress: an electroencephalography classification study.
Article in Brain communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Prevalence of hearing loss and tinnitus among school-age children: a cross-sectional analysis of the PICTURE cohort study.Frontiers in public health · 2026Article
- Prediction of acoustic tinnitus suppression using resting-state EEG via explainable AI approach.Scientific reports · 2025Article
- A scoping review of tinnitus research undertaken by New Zealand researchers: Aotearoa-an international hotspot for tinnitus innovation and collaboration.Journal of the Royal Society of New Zealand · 2025Review
- Personalized Sound Therapy Combined with Low and High-Frequency Electromagnetic Stimulation for Chronic Tinnitus.Journal of personalized medicine · 2024Article
- EEG signals from tinnitus sufferers at identifying their sound tinnitus.Data in brief · 2024Article
- Review and Perspective on Brain Bases of Tinnitus.Journal of the Association for Research in Otolaryngology : JARO · 2023Review
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.