Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus

Graph-Based Electroencephalography Analysis in Tinnitus Therapy

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers.

Hidden hearing loss: Fifteen years at a glance

Hearing loss affects approximately 18% of the population worldwide. Hearing difficulties in noisy environments without accompanying audiometric threshold shifts likely affect an even larger percentage of the global population. One of the potential causes of hidden hearing loss is cochlear synaptopathy, the loss of synapses between inner hair cells (IHC) and auditory nerve fibers (ANF). These synapses are the most vulnerable structures in the cochlea to noise exposure or aging. The loss of synapses causes auditory deafferentation, i.e., the loss of auditory afferent information, whose downstream effect is the loss of information that is sent to higher-order auditory processing stages. Understanding the physiological and perceptual effects of this early auditory deafferentation might inform interventions to prevent later, more severe hearing loss. In the past decade, a large body of work has been devoted to better understand hidden hearing loss, including the causes of hidden hearing loss, their corresponding impact on the auditory pathway, and the use of auditory physiological measures for clinical diagnosis of auditory deafferentation. This review synthesizes the findings from studies in humans and animals to answer some of the key questions in the field, and it points to gaps in knowledge that warrant more investigation. Specifically, recent studies suggest that some electrophysiological measures have the potential to function as indicators of hidden hearing loss in humans, but more research is needed for these measures to be included as part of a clinical test battery.

The human pupil and face encode sound affect and provide objective signatures of tinnitus and auditory hypersensitivity disorders

Sound is jointly processed along acoustic and emotional dimensions. These dimensions can become distorted and entangled in persons with sensory disorders, producing a spectrum of loudness hypersensitivity, phantom percepts, and - in some cases - debilitating sound aversion. Here, we looked for objective signatures of disordered hearing (DH) in the human face. Pupil dilations and micro facial movement amplitudes scaled with sound valence in neurotypical listeners but not DH participants with chronic tinnitus (phantom ringing) and sound sensitivity. In DH participants, emotionally evocative sounds elicited abnormally large pupil dilations but blunted and invariant facial reactions that jointly provided an accurate prediction of individual tinnitus and hyperacusis questionnaire handicap scores. By contrast, EEG measures of central auditory gain identified steeper neural response growth functions but no association with symptom severity. These findings highlight dysregulated affective sound processing in persons with bothersome tinnitus and sound sensitivity disorders and introduce approaches for their objective measurement.

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.