Biomarkers
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View abstract on PubMed
Summary
This summary is machine-generated.A novel speech-based method using Pinyin sequences and acoustic features improves Alzheimer's disease detection in Chinese. This approach offers a non-invasive and efficient tool for early cognitive impairment screening.
Area Of Science
- Computational linguistics
- Biomedical engineering
- Neuroscience
Background
- Alzheimer's disease (AD) diagnosis relies on invasive and costly methods, limiting early detection.
- Speech analysis offers a non-invasive alternative for cognitive impairment detection.
- Chinese homophones pose transcription challenges, potentially reducing speech-based model accuracy.
Purpose Of The Study
- To develop and validate a novel speech-based method for improved Alzheimer's disease detection in Chinese speakers.
- To overcome transcription ambiguities caused by homophones by utilizing Pinyin sequences.
- To enhance the efficiency and accuracy of early cognitive impairment screening.
Main Methods
- Utilized audio data from 300 participants in the China Preclinical Alzheimer's Disease Study (C-PAS) cohort.
- Transcribed speech to Pinyin sequences and extracted acoustic features (e.g., pause frequency, MFCCs).
- Employed a Bi-directional LSTM model to integrate Pinyin sequences, acoustic features, and demographic data, incorporating data augmentation techniques.
Main Results
- The proposed method achieved 93.80% accuracy and an AUC of 0.93.
- Integrating Pinyin sequences with acoustic features significantly outperformed models using only acoustic data or cognitive scores.
- Ablation studies confirmed the importance of combining linguistic (Pinyin) and acoustic features.
Conclusions
- Integrating Pinyin sequences and acoustic features is a feasible and effective strategy for non-invasive Alzheimer's detection in Chinese.
- This approach provides a practical tool for early Alzheimer's screening.
- Findings support further large-scale studies and potential clinical applications for cognitive impairment detection.
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