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