Character-level linguistic biomarkers for precision assessment of cognitive decline: a symbolic recurrence approach
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method using character-level speech analysis to detect early Alzheimer's disease (AD) changes. This approach identifies subtle speech patterns, offering a novel digital biomarker for dementia research.
Area Of Science
- Computational Linguistics
- Neuroscience
- Digital Health
Background
- Early-stage Alzheimer's disease (AD) detection is challenging with traditional methods.
- Subtle linguistic and cognitive changes in early AD are often missed.
- Conventional assessments may overlook individualized speech disruptions.
Purpose Of The Study
- To develop a novel biomarker discovery framework for early AD detection.
- To leverage fine-grained, character-level speech transcript information.
- To capture subtle, individualized cognitive changes associated with early dementia.
Main Methods
- Encoding speech transcripts at the character level.
- Applying Recurrence Quantification Analysis (RQA) to reveal speech dynamics (e.g., pauses, repetitions).
- Utilizing Siamese neural networks to learn embeddings from recurrence plots for biomarker discovery.
Main Results
- Uncovered meaningful character-level speech signatures in the DementiaBank corpus.
- Enabled visualization of subtle cognitive disruptions via recurrence plots.
- Identified discriminative linguistic biomarkers associated with cognitive decline.
Conclusions
- Character-level temporal speech patterns show promise as digital biomarkers for dementia.
- This approach complements traditional word-level analyses in AD research.
- The method enhances interpretability for clinical applications in neurodegenerative disease detection.
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