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An interpretable tinnitus prediction framework using gap-prepulse inhibition in auditory late response and electroencephalogram. | LitMetric

An interpretable tinnitus prediction framework using gap-prepulse inhibition in auditory late response and electroencephalogram.

Comput Methods Programs Biomed

Medical Device Research Center, Department of Biomedical Research Institute, Chungnam National University Hospital, Daejeon, Republic of Korea; Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea. Electronic address:

Published: October 2024

AI Article Synopsis

  • Tinnitus is characterized by ringing in the ears without external sound, and current diagnostic methods rely heavily on subjective assessments and complex medical exams.* -
  • The study collected EEG and auditory late response data from 44 tinnitus patients and 47 control subjects, developing a machine learning model that reached 90% accuracy in diagnosing tinnitus using specific auditory features.* -
  • Findings suggest that measuring gaps in auditory response and EEG activity could provide reliable screening tools for tinnitus, supporting clinical practices in hearing research.*

Article Abstract

Background And Objective: Tinnitus is a neuropathological condition that results in mild buzzing or ringing of the ears without an external sound source. Current tinnitus diagnostic methods often rely on subjective assessment and require intricate medical examinations. This study aimed to propose an interpretable tinnitus diagnostic framework using auditory late response (ALR) and electroencephalogram (EEG), inspired by the gap-prepulse inhibition (GPI) paradigm.

Methods: We collected spontaneous EEG and ALR data from 44 patients with tinnitus and 47 hearing loss-matched controls using specialized hardware to capture responses to sound stimuli with embedded gaps. In this cohort study of tinnitus and control groups, we examined EEG spectral and ALR features of N-P complexes, comparing the responses to gap durations of 50 and 20 ms alongside no-gap conditions. To this end, we developed an interpretable tinnitus diagnostic model using ALR and EEG metrics, boosting machine learning architecture, and explainable feature attribution approaches.

Results: Our proposed model achieved 90 % accuracy in identifying tinnitus, with an area under the performance curve of 0.89. The explainable artificial intelligence approaches have revealed gap-embedded ALR features such as the GPI ratio of N1-P2 and EEG spectral ratio, which can serve as diagnostic metrics for tinnitus. Our method successfully provides personalized prediction explanations for tinnitus diagnosis using gap-embedded auditory and neurological features.

Conclusions: Deficits in GPI alongside activity in the EEG alpha-beta ratio offer a promising screening tool for assessing tinnitus risk, aligning with current clinical insights from hearing research.

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Source
http://dx.doi.org/10.1016/j.cmpb.2024.108371DOI Listing

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