Natural language processing-based classification of early Alzheimer's disease from connected speech

The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific for the detection of AD.

We applied a language model to automatically transcribed connected speech from 114 Flemish-speaking individuals to first distinguish early AD patients from amyloid negative cognitively unimpaired (CU) and then amyloid negative from amyloid positive CU individuals using five different types of connected speech.

The language model was able to distinguish between amyloid negative CU subjects and AD patients with up to 81.9% sensitivity and 81.8% specificity. Discrimination between amyloid positive and negative CU individuals was less accurate, with up to 82.7% sensitivity and 74.0% specificity. Moreover, autobiographical interviews consistently outperformed scene descriptions.

Our findings highlight the value of autobiographical interviews for the automated analysis of connecting speech.

This study compared five types of connected speech for the detection of early Alzheimer's disease (AD). Autobiographical interviews yielded a higher specificity than scene descriptions. A preceding clinical AD classification task can refine the performance of amyloid status classification in cognitively healthy individuals.