Quantifying Consciousness for Alzheimer's Disease Diagnosis through Electroencephalogram Processing
Yuan Ma, Jeffery Keith Spaneas Bland, Gaku Yoshikawa, Tsutomu Fujinami
International Conference on Medical and Health Informatics May 17, 2024 DOI: 10.1145/3673971.3673978 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Computational modeling study using a public EEG dataset and machine learning classification Peer reviewed |
|---|---|
| Population | Healthy elderly individuals and people with Alzheimer's disease, drawn from a public EEG dataset |
| Key points | Machine learning classifiers trained on "Φ values" derived from EEG under integrated information theory, combined with additional information, distinguished healthy older adults from people with Alzheimer's disease with accuracy exceeding prior benchmark research. The authors contend that EEG-based measures of consciousness hold promise for Alzheimer's diagnosis. |
Abstract
Electroencephalograms (EEG) could serve as a biomarker for Alzheimer's disease (AD), leveraging its indicative features. However, the theoretical implications of EEG in the clinical diagnostic routine for AD remain underexplored. This study introduces an approach to analyze EEG signals within the computational framework proposed by integrated information theory, aiming to process EEG signals from a consciousness perspective for AD diagnosis. Initially, we quantified consciousness as an indicator, termed the Φ value, by analyzing a public EEG dataset comprising recordings from healthy elderly individuals and people with AD. Subsequently, we trained machine learning models using Φ values combined with additional information to classify the two population groups. Our best classification accuracy surpassed that of previous benchmark research. This study underscores the promising potential of EEG signals in measuring consciousness for diagnosing AD. As future endeavors, we outline three key aspects for exploration, including frequency domain analysis, electrode combination complexities, and temporal resolution.
Citations in the library
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