# Predicting progression from amnestic mild cognitive impairment to Alzheimer's disease using longitudinal EEG data: a 12-month cohort study

## TL;DR

This study uses EEG data collected over 12 months to predict which patients with aMCI will progress to Alzheimer's disease.

## Contribution

The study introduces longitudinal EEG features to improve machine learning prediction of aMCI progression to AD.

## Key findings

- Longitudinal EEG features showed better prediction performance than cross-sectional features.
- SVM achieved 94.92% accuracy in predicting aMCI progression to AD.
- Longitudinal features captured dynamic trends in EEG data linked to disease progression.

## Abstract

Amnestic mild cognitive impairment (aMCI), serving as a clinical precursor to Alzheimer's Disease (AD), assumes a pivotal role in the early stages of AD prevention. The longitudinal collection of data in aMCI is imperative for monitoring disease progression and guiding clinical interventions.

Utilizing a prospective cohort design, we recruited aMCI individuals and conducted a one-year follow-up study. During this period, electroencephalogram (EEG) signals were systematically collected at regular intervals, resulting in four time points for each participant. Based on the follow-up outcomes, participants were stratified into progressive mild cognitive impairment (PMCI) and stable mild cognitive impairment (SMCI) groups. We extracted spectral, nonlinear, and functional connectivity features from the EEG data at three cross-sectional time points in the initial nine months and constructed longitudinal features between these cross-sectional assessments. The longitudinal features were fed into machine learning classifiers to predict one-year follow-up outcomes.

The dynamic trends of EEG features in SMCI and PMCI patients exhibited inconsistency. Utilizing the selected longitudinal features, the support vector machine (SVM) demonstrated the best prediction performance, achieving an accuracy of 94.92%, an area under the curve of 93.25%, a sensitivity of 90.20%, a specificity of 98.80%, a positive predictive value of 98.70%, and an F1-score of 93.65%.

By capturing trend information associated with disease progression, longitudinal EEG features contributed to enhancing prediction performance in machine learning models.