Continuous EEG Trend Analysis in Intensive Care Patients
Jose Maria Perez-Macias (Software Architecture & Signal Processing)
Clinical Neuro-Monitoring & Quantitative EEG (QEEG) Algorithms
Overview
This project involves creating software to analyze EEG trends in intensive care patients, assisting in better patient monitoring and care.
Background
Continuous EEG monitoring in ICU settings helps detect subtle changes in brain activity which can be critical for conditions like delayed cerebral ischemia post-subarachnoid hemorrhage.
Aim
To develop algorithms that can automatically detect and classify EEG trends indicative of neurological deterioration, thereby enhancing patient outcomes through timely intervention.
Methods
We employ two primary methods for EEG analysis:
- Alpha Delta Ratio (ADR): Calculates the ratio of alpha to delta wave activity, with separate hemispheric analysis.
- Relative Alpha Variability (RAV): Measures variability in alpha wave activity to grade the severity of EEG changes.
Both methods use moving windows with overlapping to ensure continuous monitoring and alarm triggering for critical changes.
Results
Preliminary results indicate that these methods can effectively identify early signs of brain ischemia or other neurological disturbances, potentially improving patient management in ICU settings.
Resources & Software Architecture
This clinical decision-support pipeline automates quantitative EEG (QEEG) feature calculation, providing real-time visual trending and threshold alarms for intensive care teams.
- Author & Developer: Jose Maria Perez-Macias (Aalto University)
- Codebase Status: Signal processing algorithms and GUI pipeline developed in 2021. The repository is slated for public release on GitHub.
- Author ORCID: 0000-0003-3485-0874
Clinical Reference Literature
The project methods build upon the following clinical neurophysiology literature:
- Wickering, E. et al. Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia. J Clin Neurophysiol 33, 227–234 (2016).
- Muniz, C. F. et al. Clinical Development and Implementation of an Institutional Guideline for Prospective EEG Monitoring and Reporting of Delayed Cerebral Ischemia. J Clin Neurophysiol 33, 217–226 (2016).
- Rots, M. L., et al. Continuous EEG Monitoring for Early Detection of Delayed Cerebral Ischemia in Subarachnoid Hemorrhage: A Pilot Study. Neurocrit Care 24, 207–216 (2016).
- Gollwitzer, S. et al. Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: A prospective study in adults. Clin Neurophysiol 126, 1514–1523 (2015).
- Labar, D. R., et al. Quantitative EEG monitoring for patients with subarachnoid hemorrhage. Electroen Clin Neuro 78, 325–332 (1991).
- Samagh, N., et al. Monitoring cerebral vasospasm: How much can we rely on transcranial Doppler. J Anaesthesiol Clin Pharmacol 35, 12–18 (2019).
- Rosenthal, E. S. et al. Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy. Ann Neurol 83, 958–969 (2018).
- Claassen, J. et al. Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage. Clin Neurophysiol 115, 2699–2710 (2004).
- Vespa, P. M. et al. Early detection of vasospasm after acute subarachnoid hemorrhage using continuous EEG ICU monitoring. Electroen Clin Neuro 103, 607–615 (1997).