Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning
Article
Article Title | Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning |
---|---|
ERA Journal ID | 34304 |
Article Category | Article |
Authors | Doborjeh, Maryam, Liu, Xiaoxu, Doborjeh, Zohreh, Shen, Yuanyuan, Searchfield, Grant, Sanders, Philip, Wang, Grace Y., Sumich, Alaxander and Yan, Wei Qi |
Journal Title | Sensors |
Journal Citation | 23 (2), pp. 1-17 |
Article Number | 902 |
Number of Pages | 17 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s23020902 |
Web Address (URL) | https://www.mdpi.com/1424-8220/23/2/902 |
Abstract | Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home. |
Keywords | tinnitus; artificial intelligence; EEG; prediction; TFI; functional connectivity; deep learning; digital health |
ANZSRC Field of Research 2020 | 520499. Cognitive and computational psychology not elsewhere classified |
520206. Psychophysiology | |
Byline Affiliations | Auckland University of Technology, New Zealand |
University of Auckland, New Zealand | |
University of Waikato, New Zealand | |
School of Psychology and Wellbeing | |
Centre for Health Research | |
Nottingham Trent University, United Kingdom |
https://research.usq.edu.au/item/v88w2/prediction-of-tinnitus-treatment-outcomes-based-on-eeg-sensors-and-tfi-score-using-deep-learning
Download files
81
total views32
total downloads1
views this month0
downloads this month