Prediction of tinnitus treatment outcomes based on EEG sensors and TFI score using deep learning

Doborjeh, M, Liu, X, Doborjeh, Z, Shen, Y, Searchfield, G, Sanders, P, Wang, GY, Sumich, A ORCID: https://orcid.org/0000-0003-4333-8442 and Yan, WQ, 2023. Prediction of tinnitus treatment outcomes based on EEG sensors and TFI score using deep learning. Sensors, 23 (2): 902. ISSN 1424-8220

Preview

Text 1635263_Sumich.pdf - Published version Download (6MB) | Preview

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.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Doborjeh, M., Liu, X., Doborjeh, Z., Shen, Y., Searchfield, G., Sanders, P., Wang, G.Y., Sumich, A. and Yan, W.Q.
Publisher:	MDPI AG
Date:	12 January 2023
Volume:	23
Number:	2
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s23020902 DOI 1635263 Other
Rights:	© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Social Sciences
Record created by:	Laura Ward
Date Added:	19 Jan 2023 13:52
Last Modified:	19 Jan 2023 13:52
URI:	https://irep.ntu.ac.uk/id/eprint/47970

Actions (login required)

Edit View

Statistics

Views

Downloads