Non-linear and time-domain sleep qEEG features predict CSF protein damage markers in early Alzheimer’s disease
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Introduction
In preclinical Alzheimer’s disease (AD), oxidative stress induces non-enzymatic protein damage—detected as cerebrospinal fluid (CSF) biomarkers—and disrupts sleep-related networks, altering sleep electroencephalographic patterns. Due to the invasiveness of CSF sampling, quantitative electroencephalography (qEEG) is proposed as a non-invasive alternative for predicting oxidatively modified protein levels via Machine Learning (ML).
Methods
Forty-two mild-to-moderate AD patients underwent polysomnography (PSG). qEEG features were extracted. CSF protein oxidation markers levels —glutamic semialdehyde, aminoadipic semialdehyde, N ε -carboxyethyl-lysine, N ε- carboxymethyl-lysine, and N ε -malondialdehyde-lysine —were assessed by gas chromatography/mass spectrometry, and ML models trained to predict CSF biomarker levels.
Results
qEEG features from slow-wave sleep (SWS) and rapid eye movement (REM) sleep, particularly over frontal and central regions, yielded R 2 > 0.9 and RMSE < 0.1 for biomarker prediction.
Conclusion
qEEG is a non-invasive, scalable tool for detecting AD-related oxidative stress, with potential implications for early diagnosis and risk stratification.
