| Abstrakt: |
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease. Like for other dementias, biomarkers may help characterize distinct aspects of the underlying pathology, predict decline, and monitor disease progression. We extracted a composite array of 24 speech‐based biomarkers using machine learning and signal processing techniques. We then determined which of the given biomarkers was predictive of MCI and AD progression from baseline by conducting a correlation analysis. Method: Spoken responses to a Cookie Theft Picture description task were recorded from 2 subjects with AD, 4 subjects with Mild Cognitive Impairment (MCI) due to AD (biomarker confirmed AD), and 4 subjects with MCI. We automatically extracted acoustic, linguistic, and cognitive biomarkers using speech recognition, acoustic, and language modeling techniques. We later computed Kendall's tau‐b (τb) correlation between the biomarker values and Montreal Cognitive Assessment (MoCA) scores. Recordings and MoCA scores were collected at baseline, at 6 months after baseline, and at 12 months for a subgroup of the subjects. To perform the correlation analysis, we used scipy.stats.kendalltau library in Python. Result: With respect to the acoustic biomarkers, a strong negative correlation (τb= ‐0.50) was observed between the MoCA scores and features such as pause time, pause percentage, and pause speech ratio, while a moderate positive correlation (τb= 0.2) was found for rhythm standard deviation. With respect to the linguistic biomarkers a strong positive correlation(τb= 0.50) for the features of corrected type‐token ratio, average sentence length in words, and noun count was reported, while a moderate positive correlation (τb= 0.27) was found for the features of word type count, word token count, and moving average type‐token ratio. The other acoustic, linguistic, and cognitive biomarkers showed a very weak (τb= 0.00 ‐ 0.10) or weak correlation (τb= 0.10 ‐ 0.20). Conclusion: Altogether, these results suggest that speech‐based interpretable biomarkers may help clinicians to diagnose AD at earlier stages and monitor disease progression. Our preliminary data suggest that AD patients encounter more problems delivering longer and linguistically elaborated narratives as the disease progresses. [ABSTRACT FROM AUTHOR] |
