Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis

Li, Yun; Liu, Zhe; Yao, Lina; Monaghan, Jessica J. M.; McAlpine, David

doi:10.48550/arxiv.2205.03230

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…In addition to its application in tinnitus, EEG has also attracted much attention in other fields, where advanced deep learning methods are used [28], [29], [30], [31], [32]. For example, DeepSleepNet [28] used convolutional neural networks (CNN) and bidirectional-long short-termmemory (LSTM) to automatically score sleep stage based on EEG signals.…”

Section: B Cross-dataset Eeg Research In Related Fieldsmentioning

confidence: 99%

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

Liu

Yao

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 metalearning in improving the quality of the learned features.

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Section: B Cross-dataset Eeg Research In Related Fieldsmentioning

confidence: 99%

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

Liu

Yao

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…However, due to the complex central mechanism of tinnitus and numerous influencing factors, many previous studies have not reached comprehensive consistent results. Therefore, many attempts have been made in diagnosing tinnitus based on the EEG signals by using machine learning and deep learning methods [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

Wang

Zhu

Zhou

et al. 2023

IEEE J. Biomed. Health Inform.

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Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

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Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis

Cited by 2 publications

References 58 publications

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

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

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

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