PDF(Pubmed)
Abstract：
In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients\' overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, principal component analysis (PCA) is employed for feature reduction, enhancing interpretability. The proposed approach undergoes evaluation using ensemble learning techniques, including Random Forest, Extra Trees, Gradient Boosting, and CatBoost. The performance of the proposed models is scrutinized across a comprehensive set of metrics, encompassing cross-validation accuracy (CVA), precision, recall, F1-score, Kappa, and Matthews correlation coefficient (MCC). The proposed models demonstrate statistical significance and effectively diagnose auditory disorders, contributing to early detection and personalized treatment, thereby enhancing patient outcomes and quality of life. Notably, they exhibit reliability and robustness, characterized by high Kappa and MCC values. This research represents a significant advancement in the intersection of audiology, neuroimaging, and machine learning, with transformative implications for clinical practice and care.
摘要：
在听力学领域，实现听觉障碍的准确辨别仍然是一个巨大的挑战。耳聋和耳鸣等情况对患者的整体生活质量产生重大影响，强调迫切需要精确有效的分类方法。这项研究引入了一种创新的方法，利用从三个不同队列获得的多视图脑网络数据：51名聋哑患者，54伴有耳鸣，和42个正常对照。精心收集脑电图（EEG）记录数据，聚焦于连接到具有10个感兴趣区域(ROI)的端到端密钥的70个电极。这些数据与机器学习算法协同集成。为了解决大脑连接数据固有的高维性质，主成分分析(PCA)用于特征约简，增强可解释性。所提出的方法使用集成学习技术进行评估，包括随机森林，额外的树木，梯度提升，和CatBoost。建议的模型的性能经过了一系列全面的指标审查，包括交叉验证准确性(CVA)，精度,召回，F1分数，Kappa,和马修斯相关系数(MCC)。所提出的模型显示出统计意义，并有效地诊断听觉障碍，有助于早期发现和个性化治疗，从而提高患者的治疗效果和生活质量。值得注意的是,它们表现出可靠性和鲁棒性，具有高Kappa和MCC值。这项研究代表了听力学交叉的重大进展，神经影像学,和机器学习，对临床实践和护理具有变革性意义。
