Prediction of the outcome in cardiac arrest patients undergoing hypothermia using EEG wavelet entropy

Abstract

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients' outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (p-value<; 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia.

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy*

“…We found that information theoretic measures of EEG complexity and regularity were predictive of good functional outcomes while features measuring epileptiform discharges were associated with poor outcomes. (5,17,19,22,26,41,42) Two EEG complexity features (Cepstrum and Tsallis entropy) and EEG regularity contributed to predictions in the first 24 hours of monitoring. These findings substantiate prior reports that specific EEG signatures observed during the first 24 hours after cardiac arrest have strong predictive value despite hypothermia and sedative use.…”

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

“…According to the complexity theories, somewhat higher complexity is associated with relatively improved health conditions and greater chances of survival (Costa et al, 2002a , 2005 ), while a reduction in or loss of complexity is often associated with imbalance or disturbed physiological conditions, usually implying disease or aging (Goldberger et al, 2002 ). In many studies, complexity-based metrics of the dynamics of a physiological system have demonstrated better prognostic power (Mejaddam et al, 2013 ; Lin et al, 2014 ; Vandendriessche et al, 2014 ; Moshirvaziri et al, 2016 ; Chiu et al, 2017 ; Ma et al, 2017 ). The complexity theories also suggest that different levels of complexity can indicate whether a system is under stress or relatively relaxed (Costa et al, 2002a , 2005 ; Goldberger et al, 2002 ).…”