Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

• URL: http://arxiv.org/abs/2205.03231v1
• Date: Tue, 3 May 2022 03:17:44 GMT
• Title: Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
• Authors: Yun Li, Zhe Liu, Lina Yao, Molly Lucas, Jessica J.M.Monaghan, and Yu Zhang
• Abstract summary: This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis. Our method achieves a high accuracy of 73.8% in the cross-dataset classification.
• Score: 38.66127142638335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders from different data collection processes. Owing to the superiority of meta-learning, our method does not rely on large-scale datasets like conventional deep learning models. Moreover, we design a subject-specific training process to assist the model in fitting the data pattern of different patients or healthy people. Our method achieves a high accuracy of 73.8\% in the cross-dataset classification. We conduct an extensive analysis to show the effectiveness of side information of ears in enhancing model performance and side-aware meta-learning in improving the quality of the learned features.

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