Alzheimer's disease recognition based on waveform and spectral speech signal processing
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View abstract on PubMed
Summary
This summary is machine-generated.Speech analysis offers a new way to detect Alzheimer's disease (AD). A novel AD recognition network (ADNet) analyzes speech features, showing promise for early AD detection and distinguishing it from normal cognition.
Area Of Science
- Neuroscience
- Computational Linguistics
- Medical Informatics
Background
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder.
- Current AD diagnosis relies on invasive and expensive methods.
- Early detection of AD is crucial for effective management.
Purpose Of The Study
- To propose a novel multi-channel network framework (ADNet) for Alzheimer's disease recognition using speech analysis.
- To investigate the efficacy of integrating time-domain and frequency-domain speech features for AD detection.
Main Methods
- Developed ADNet, a multi-channel network integrating time-domain and frequency-domain speech features.
- Utilized waveform images and log-Mel spectrograms as speech signal data sources.
- Employed inverted residual blocks and gated multi-information units for feature learning and integration.
Main Results
- The proposed ADNet achieved high performance on the Shanghai Cognitive Screening (SCS) dataset.
- Achieved an accuracy of 88.57%, precision of 88.67%, and recall of 88.64%.
- Outperformed existing speech-based methods for Alzheimer's disease recognition.
Conclusions
- The ADNet framework effectively distinguishes Alzheimer's disease patients from healthy controls.
- Speech analysis, particularly using the proposed ADNet, shows significant potential for early AD recognition tools.
- This approach offers a non-invasive and potentially cost-effective method for AD screening.
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