| Abstract: | Seizures, status epilepticus, and seizure-like rhythmic or periodic activity are common, pathological, and harmful states of brain electrical activity seen in the electroencephalogram (EEG) of patients during critical medical illnesses or acute brain injury. In this study, we aimed to develop a valid method to automatically discover a small number of homogeneous pattern clusters, to facilitate efficient interactive labelling by EEG experts. Long term continuous EEG of ten ICU patients at MGH were analysed, undergoing the pipeline of feature extraction, PCA-based dimensionality reduction, and embedding through LE map. This research suggests that large EEG datasets can be automatically clustered into a small number of patterns described by standard ICU EEG pattern labels. We demonstrated efficient cluster labelling by inspecting only the centroids of clusters. Furthermore, LE visualizations support the hypothesis of an interictal-ictal continuum. |
