Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 2352 - 2361
• Main Authors: Liu, Zhe, Li, Yun, Yao, Lina, Lucas, Molly, Monaghan, Jessica J. M., Zhang, Yu
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; cross-dataset; Data collection; Datasets; Deep learning; Diagnosis; Ear; EEG; Electroencephalography; Learning algorithms; Machine learning; Medical diagnosis; meta-learning; Signs and symptoms; subject-independent; Task analysis; Tinnitus; Training
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3201158

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.