EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods

Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, similar to 300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.

Automated summary

Lateral brain symmetry indexes, detected by EEG, are widely used markers of rehabilitation recovery in patients with severe acquired brain injury. This study used Machine Learning algorithms to detect consistent asymmetries in EEG recordings of 54 patients. The best performing solution achieved a test accuracy of 85% in detecting asymmetry. The proposed algorithm has the potential to improve the efficiency of long EEG analysis and enhance the quality of care in settings lacking skilled staff.