Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
Medical Device Research Center, Department of Biomedical Research Institute, Chungnam National University Hospital, Daejeon, Republic of Korea.
Comput Methods Programs Biomed. 2024 Oct;255:108371. doi: 10.1016/j.cmpb.2024.108371. Epub 2024 Aug 21.
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.
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.
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.
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.
耳鸣是一种导致耳朵出现轻微嗡嗡声或铃声但无外部声源的神经病理学状况。目前的耳鸣诊断方法通常依赖于主观评估，并且需要复杂的医学检查。本研究旨在提出一种使用听觉后反应（ALR）和脑电图（EEG）的可解释性耳鸣诊断框架，该框架受间隙预脉冲抑制（GPI）范式的启发。
我们使用专门的硬件从 44 名耳鸣患者和 47 名听力损失匹配的对照组中收集自发 EEG 和 ALR 数据，以捕获带有嵌入式间隙的声音刺激的反应。在这项耳鸣和对照组的队列研究中，我们检查了 N-P 复合体的 EEG 频谱和 ALR 特征，比较了 50ms 和 20ms 间隙以及无间隙条件下的反应。为此，我们使用 ALR 和 EEG 指标、提升机器学习架构和可解释特征归因方法开发了一种可解释的耳鸣诊断模型。
我们提出的模型在识别耳鸣方面达到了 90%的准确率，性能曲线下面积为 0.89。可解释的人工智能方法揭示了嵌入间隙的 ALR 特征，例如 N1-P2 的 GPI 比和 EEG 频谱比，它们可以作为耳鸣的诊断指标。我们的方法成功地使用嵌入间隙的听觉和神经特征为耳鸣诊断提供了个性化的预测解释。
GPI 缺陷以及 EEGα-β 比的活动为评估耳鸣风险提供了一种有前途的筛选工具，与听力研究的当前临床见解一致。
