METHODS: Both instruments were operated according to manufacturer's instructions. Samples used include a commercially available normal control serum (NCS) and patients' specimens. The following were evaluated: precision and comparison studies for SPE, and reproducibility and comparison studies for IFE. Statistical analyses were performed using Microsoft Excel.

RESULTS: For SPE repeatability study, our results showed that EFG26 has higher coefficient of variation (%CV) compared with H2SCAN for both samples except for monoclonal component with %CV of 0.97% and 1.18%, respectively. Similar results were obtained for SPE reproducibility study except for alpha-1 (4.16%) and beta (3.13%) fractions for NCS, and beta fractions (5.36%) for monoclonal sample. Subsequently, reproducibility for IFE was 100% for both instruments. Values for correlation coefficients between both instruments ranged from 0.91 to 0.98 for the five classic bands.

METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.

RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.