Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-consuming, require manual interpretation by trained clinicians, suffer from low ecological validity, and diagnosis can be biased by comorbid motor and cognitive problems present in aphasia. In this study, we introduce an automated screening tool for speech processing impairments in aphasia that relies on time-locked brain responses to speech, known as neural tracking, within a deep learning framework. We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech. Subsequently, we evaluated our models on an independent sample comprising 26 individuals with aphasia (IWA) and 22 healthy controls. Our results reveal decreased tracking of all speech representations in IWA. Utilizing a support vector machine classifier with neural tracking measures as input, we demonstrate high accuracy in aphasia detection at the individual level (85.42\%) in a time-efficient manner (requiring 9 minutes of EEG data). Given its high robustness, time efficiency, and generalizability to unseen data, our approach holds significant promise for clinical applications.

翻译：失语症是一种主要由脑卒中引起的语言障碍，传统上通过行为语言测试进行诊断。然而，这些测试耗时长、需要经过训练的临床医生进行人工解读、生态效度低，且诊断可能受到失语症患者共存的运动和认知问题的干扰。本研究提出了一种基于深度学习框架的失语症语音处理障碍自动筛查工具，该工具利用与语音锁时的脑电响应（即神经追踪）进行分析。我们使用卷积神经网络，以大规模健康参与者样本进行训练，模拟了健康人对故事中声学、分段和语言语音表征的脑电图响应模型，该模型可作为完整语音神经追踪的参照。随后，我们在包含26名失语症患者和22名健康对照者的独立样本上评估了模型。结果显示，失语症患者对所有语音表征的追踪能力均出现下降。利用支持向量机分类器以神经追踪指标作为输入，我们实现了在个体层面高准确率（85.42%）的失语症检测，且耗时短（仅需9分钟脑电图数据）。鉴于该方法具有高鲁棒性、时间效率高以及对未知数据的泛化能力，其在临床应用中具有显著潜力。