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

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy

OBJECTIVES

Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions.

DESIGN

Retrospective.

SETTING

ICUs at four academic medical centers in the United States.

PATIENTS

Comatose patients with acute hypoxic-ischemic encephalopathy.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated.

CONCLUSIONS

The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance.

The EEG Analysis of Actual Left/Right Lateral Bending Movements in Patient of Lumbar Disc Herniation

The purpose of this study was to investigate surface EEG of two groups (lumbar disc herniation (LDH) patients and healthy controls (HC)) when they performed real smooth movements (left/right lateral bending) with the maximum voluntary movement without pain. Thirty female LDH patients and thirty healthy controls volunteered to participate in the experiment. We also tested a healthy participant's motion imaginery (MI) of left/right lateral bending movement for over 200 times. We used Daubechies 4 (db4) wavelet to decompose EEG signal and we extracted δ, θ, α and β rhythms of the EEG signal. Wavelet entropy and sample entropy (SampEn) of four frequency bands were calculated. The results showed that there was significant difference of wavelet entropy EEG in T7, O2, and AF4 channels between the LDH and the healthy group when they did real left lateral bending. The topographic map also showed that SampEn value of four rhythms of the MI right lateral bending were significantly less than the values of the MI left lateral motion in healthy participant. Classification and Regression Trees (CART), Logistic regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), and the Linear discrimination analysis (LDA) classifiers showed averaged accuracies more than 96% for MI of left/right lateral bending.

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

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI₁₋₃₀. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.