S85. Automated quantitative EEG reactivity testing using actigraphy monitoring in cardiac arrest coma prognostication. (May 2018)

• Record Type: Journal Article
• Authors: Amorim, Edilberto; Nagaraj, Sunil; Alvarez, Vincent; Van Der Stoel, Michelle; Ghassemi, Mohammad; Mushrif, Shreyas; Cash, Sydney; Westover, M. Brandon; Lee, Jong Woo
• Is Part Of: Clinical neurophysiology. Volume 129(2018)Supplement 1
• Page Start: e173
• Page End: e174
• Publication Date: 2018-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.445
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Introduction: EEG background reactivity is a strong predictor of coma recovery after cardiac arrest. The current clinical value of EEG background reactivity testing is limited by inadequate inter-rater expert agreement, few number of daily assessments, and unsuitability for quantitative tracking by visual review. We hypothesized that a quantitative EEG reactivity method using actigraphy-triggered events from a wrist-worn wearable can predict long-term functional outcome in comatose cardiac arrest patients treated with targeted temperature management. Methods: We prospectively recorded clinical, actigraphy, and EEG data of comatose cardiac arrest patients managed with targeted temperature management at a single U.S. academic hospital. Continuous actigraphy data obtained from a wrist-worn wearable (Affectiva 3) was synchronized to continuous EEG data for up to 96 h after cardiac arrest. Change in actigraphy across time was used as a surrogate of bedside external physical stimulation (i.e. actigraphy-triggered event). Our quantitative EEG method evaluated changes in EEG spectra, entropy, and frequency features during 30 s before and 30 s after each actigraphy-triggered event (46 EEG features used). Actigraphy threshold and EEG window duration (pre and post actigraphy-triggered events) were utilized as hyperparameters in the model. In addition to actigraphy-triggered quantitative EEG background reactivity, EEG reactivity was scored visually as present or absent once daily by a single expert electroencephalographer during clinical care. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. Classification was carried out using extreme learning machine and leave-one-subject-out cross validation. Final outcome class prediction for each individual subject was determined using simple majority voting across all actigraphy-triggered events. Results: Ten subjects were monitored. Mean age was 60 (standard deviation (SD) ± 14.6) years, 10% were female, 50% had a shockable rhythm, and 40% had good outcome at six months. A total of 19 (SD ± 8.9) actigraphy-triggered events per patient were detected. Best outcome prediction performance was achieved using 50-s-long EEG window (25-s pre and 25-s post each actigraphy-triggered event). The quantitative EEG reactivity method correctly predicted good outcome in 80% of cases (sensitivity and specificity 80%). Expert rating using once daily visual EEG reactivity assessments had 60% accuracy for good outcome prediction. Conclusion: Quantitative EEG reactivity assessments using actigraphy-based triggered events is feasible and may support long-term outcome prediction in cardiac arrest coma. Integration of EEG and wearable sensors using machine learning methods might facilitate the deployment of dynamic multi-modal patient monitoring in the intensive care unit environment.