MATERIAL AND METHODS: This is a retrospective study of epilepsy cases with VEM performed in University Malaya Medical Center (UMMC), Kuala Lumpur, from January 2012 till August 2016.

RESULTS: A total of 137 cases were included. The mean age was 34.5 years old (range 15-62) and 76 (55.8 %) were male. On the first 24 -h of recording (D1), 81 cases (59.1 %) had seizure occurrence, and 109 (79.6 %) by day 2 (D2). One-hundred and nine VEMs (79.6 %) were diagnostic, in guiding surgical decision or further investigations. Of these, 21 had less than 2 seizures recorded in the first 48 h but were considered as diagnostic because of concordant interictal ± ictal activities, or a diagnosis such as psychogenic non-epileptic seizure was made. Twenty-eight patients had extension of VEM for another 24-48 h, and 11 developed seizures during the extension period. Extra-temporal lobe epilepsy and seizure frequency were significant predictors for diagnostic 48 -h VEM. Three patients developed complications, including status epilepticus required anaesthetic agents (1), seizure clusters (2) with postictal psychosis or dysphasia, and all recovered subsequently.

METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

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.