A quantitative EEG index for the recognition of arterial ischemic stroke in children.

OBJECTIVE: To describe and assess performance of the Correlate Of Injury to the Nervous system (COIN) index, a quantitative electroencephalography (EEG) metric designed to identify areas of cerebral dysfunction concerning for stroke. METHODS: Case-control study comparing continuous EEG data from children with acute ischemic stroke to children without stroke, with or without encephalopathy. COIN is calculated continuously and compares EEG power between cerebral hemispheres. Stroke relative infarct volume (RIV) was calculated from quantitative neuroimaging analysis. Significance was determined using a two-sample t-test. Sensitivity, specificity, and accuracy were measured using logistic regression. RESULTS: Average COIN values were -34.7 in the stroke cohort compared to -9.5 in controls without encephalopathy (p = 0.003) and -10.5 in controls with encephalopathy (p = 0.006). The optimal COIN cutoff to discriminate stroke from controls was -15 in non-encephalopathic and -18 in encephalopathic controls with >92% accuracy in strokes with RIV > 5%. A COIN cutoff of -20 allowed discrimination between strokes with <5% and >5% RIV (p = 0.027). CONCLUSIONS: We demonstrate that COIN can identify children with acute ischemic stroke. SIGNIFICANCE: COIN may be a valuable tool for stroke identification in children. Additional studies are needed to determine utility as a monitoring technique for children at risk for stroke.

The International Cardiac Arrest Research Consortium Electroencephalography Database.

OBJECTIVES: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN: Multicenter cohort, partly prospective and partly retrospective. SETTING: Seven academic or teaching hospitals from the United States and Europe. PATIENTS: Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS: The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database.

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design: Multicenter cohort, partly prospective and partly retrospective. Setting: Seven academic or teaching hospitals from the U.S. and Europe. Patients: Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions: not applicable. Measurements and Main Results: Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions: The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

Prediction of patient survival following postanoxic coma using EEG data and clinical features.

Electroencephalography (EEG) is an effective and non-invasive technique commonly used to monitor brain activity and assist in outcome prediction for comatose patients post cardiac arrest. EEG data may demonstrate patterns associated with poor neurological outcome for patients with hypoxic injury. Thus, both quantitative EEG (qEEG) and clinical data contain prognostic information for patient outcome. In this study we use machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) to classify patient outcome post cardiac arrest using qEEG and clinical feature sets, individually and combined. Our ML experiments show RF and SVM perform better using the joint feature set. In addition, we extend our work by implementing a convolutional neural network (CNN) based on time-frequency images derived from EEG to compare with our qEEG ML models. The results demonstrate significant performance improvement in outcome prediction using non-feature based CNN compared to our feature based ML models. Implementation of ML and DL methods in clinical practice have the potential to improve reliability of traditional qualitative assessments for postanoxic coma patients.