A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Abstract

BACKGROUND: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

“…Seizures are bursts of uncontrolled electrical activity between brain cells, causing temporary abnormalities in the function of some organs. The random forest (RF) model was used to diagnose and monitor traumatic brain injuries related to seizures in a study (13); however, another study (14) used the generalized linear model (GLM).…”

“…The first subcategory includes studies aiming to diagnose and predict the occurrence of pain during or after an operation. For example, in a study (13), the possibility of diagnosing toothache based on the three signals of electrocardiography (ECG), photoplethysmography (PPG), and chest were investigated using the RF model, which performed well on the test dataset. Tan et al (17) compared ML techniques to statistical inference techniques to identify and predict breakthrough pain during labor, with the J Cell Mol Anesth.…”