Prognostication of Neurological Recovery by Analyzing Structural Breaks in EEG Data

We describe an approach for unsupervised, multivariate yet interpretable structural break testing of rich electroencephalographic (EEG) data time series to perform early prediction of patient outcome after resuscitation from cardiac arrest. Few models exist that reliably determine prognosis among comatose post-arrest patients within hours of hospital admission. We present an efficient method designed to detect anomalous patterns in streaming EEG data that combines scan statistics with multiple structural break tests. Some patterns of change show non-trivial power in prognosticating patient outcomes at clinically relevant prediction horizons. Empirical evaluation of the proposed method shows its potential utility in determining cardiac arrest patient outcomes earlier and more confidently than existing alternatives.