基于语音的机器学习在检测认知能力下降和疑似阿尔茨海默病方面具有强大的功能，适用于一系列不同领域的语言特征，但这些领域之间也存在显著差异。
引用: He, R., Chapin, K., Al-Tamimi, J., Bel, N., Marquié, M., Rosende-Roca, M., Pytel, V., Tartari, J. P., Alegret, M., Sanabria, A., Ruiz, A., Boada, M., Valero, S., & Hinzen, W. (2023). Automated Classification of Cognitive Decline and Probable Alzheimer’s Dementia Across Multiple Speech and Language Domains. American journal of speech-language pathology, 32(5), 2075–2086. https://doi.org/10.1044/2023_AJSLP-22-00403
Learning outcomes:
- Machine learning based on automatically extracted language features detected cognitive decline from early stages of the AD continuum in a new Spanish-Catalan dataset.
- Different speech and language domains showed differential discrimination performance between groups, with features extracted directly from speech performing better than those from the text.
- Before the onset of objective cognitive impairment, speech and language from older adults with Subjective Cognitive Decline (SCD) showed speech and language differences from controls without SCD, indicating potential heterogeneity in these non-clinical groups.
Abstract
Background: Decline in language has emerged as a new potential biomarker for the early detection of Alzheimer’s disease (AD). It remains unclear how sensitive language measures are across different tasks, language domains, and languages, and to what extent changes can be reliably detected in early stages such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI).
Method: Using a scene construction task for speech elicitation in a new Spanish/Catalan speaking cohort (N = 119), we automatically extracted features across seven domains, three acoustic (spectral, cepstral, and voice quality), one prosodic, and three from text (morpholexical, semantic, and syntactic). They were forwarded to a random forest classifier to evaluate the discriminability of participants with probable AD dementia, amnestic and nonamnestic MCI, SCD, and cognitively healthy controls. Repeated-measures analyses of variance and paired-samples Wilcoxon signed-ranks test were used to assess whether and how performance differs significantly across groups and linguistic domains.
Results: The performance scores of the machine learning classifier were generally satisfactorily high, with the highest scores over .9. Model performance was significantly different for linguistic domains (p < .001), and speech versus text (p = .043), with speech features outperforming textual features, and voice quality performing best. High diagnostic classification accuracies were seen even within both cognitively healthy (controls vs. SCD) and MCI (amnestic and nonamnestic) groups.
Discussion: Speech-based machine learning is powerful in detecting cognitive decline and probable AD dementia across a range of different feature domains, though important differences exist between these domains as well.
以下为机器翻译摘要：
背景： 语言能力下降已成为早期检测阿尔茨海默病（AD）的一种新的潜在生物标志物。目前仍不清楚语言测量在不同任务、语言领域和语言中的敏感度如何，也不清楚在主观认知能力下降（SCD）和轻度认知障碍（MCI）等早期阶段能在多大程度上可靠地检测到语言的变化。
方法： 我们在一个新的西班牙语/加泰罗尼亚语队列（N = 119）中使用场景构建任务进行语音诱导，自动提取了七个领域的特征，其中三个是声学特征（频谱、倒频谱和语音质量），一个是韵律特征，三个是从文本中提取的特征（语法、语义和句法）。这些数据被传入随机森林分类器，以评估参与者对可能患有注意力缺失性痴呆症、有记忆力和无记忆力 MCI、SCD 以及认知健康对照组的辨别能力。重复测量方差分析和配对样本Wilcoxon符号秩检验用于评估不同群体和语言领域的表现是否存在显著差异以及差异如何。
结果： 机器学习分类器的性能得分普遍较高，最高得分超过 0.9。在语言域（p < .001）和语音与文本（p = .043）方面，模型性能有明显差异，语音特征优于文本特征，语音质量表现最佳。即使在认知健康组（对照组与 SCD 组）和 MCI 组（有记忆障碍组与无记忆障碍组）中，诊断分类的准确率也很高。
讨论： 基于语音的机器学习在检测认知能力下降和可能的阿尔茨海默病方面具有强大的功能，适用于一系列不同的特征领域，但这些领域之间也存在重要差异。