Abstract：
The tremors of Parkinson\'s disease (PD) and essential tremor (ET) are known to have overlapping characteristics that make it complicated for clinicians to distinguish them. While deep learning is robust in detecting features unnoticeable to humans, an opaque trained model is impractical in clinical scenarios as coincidental correlations in the training data may be used by the model to make classifications, which may result in misdiagnosis. This work aims to overcome the aforementioned challenge of deep learning models by introducing a multilayer BiLSTM network with explainable AI (XAI) that can better explain tremulous characteristics and quantify the respective discovered important regions in tremor differentiation. The proposed network classifies PD, ET, and normal tremors during drinking actions and derives the contribution from tremor characteristics, (i.e., time, frequency, amplitude, and actions) utilized in the classification task. The analysis shows that the XAI-BiLSTM marks the regions with high tremor amplitude as important in classification, which is verified by a high correlation between relevance distribution and tremor displacement amplitude. The XAI-BiLSTM discovered that the transition phases from arm resting to lifting (during the drinking cycle) is the most important action to classify tremors. Additionally, the XAI-BiLSTM reveals frequency ranges that only contribute to the classification of one tremor class, which may be the potential distinctive feature to overcome the overlapping frequencies problem. By revealing critical timing and frequency patterns unique to PD and ET tremors, this proposed XAI-BiLSTM model enables clinicians to make more informed classifications, potentially reducing misclassification rates and improving treatment outcomes.
摘要：
已知帕金森病（PD）和特发性震颤（ET）的震颤具有重叠的特征，这使得临床医生难以区分它们。虽然深度学习在检测人类不明显的特征方面是强大的，不透明的训练模型在临床场景中是不切实际的，因为训练数据中的巧合相关性可能被模型用来进行分类，这可能导致误诊。这项工作旨在通过引入具有可解释AI（XAI）的多层BiLSTM网络来克服深度学习模型的上述挑战，该网络可以更好地解释颤抖特征并量化震颤分化中各自发现的重要区域。拟议的网络对PD进行了分类，ET,和饮酒过程中的正常震颤，并从震颤特征中获得贡献，(即，时间,频率,振幅,和动作)在分类任务中使用。分析表明，XAI-BiLSTM标记具有高震颤振幅的区域在分类中很重要，通过相关性分布与震颤位移幅度之间的高度相关性来验证。XAI-BiLSTM发现，从手臂休息到抬起（在饮酒周期中）的过渡阶段是对震颤进行分类的最重要动作。此外,XAI-BiLSTM揭示了仅有助于一种震颤类别分类的频率范围，这可能是克服重叠频率问题的潜在独特特征。通过揭示PD和ET震颤特有的关键时间和频率模式，这个提出的XAI-BiLSTM模型使临床医生能够做出更明智的分类，有可能降低误分类率并改善治疗结果.
