Lack of Continuous Video EEG Surveillance Results in Delayed Event Reporting.

Although real-time event detection during video EEG recording is required to ensure patients' safety, it is limited by the technologists' availability. We sought to explore the efficiency of real-time event detection by the EEG technologists in a single tertiary academic center. We retrospectively reviewed events from continuous inpatient video EEGs (cEEGs) and epilepsy monitoring unit (EMU) recordings in January 2017, when real-time surveillance was only available during the night shift, and June 2017, when a dedicated neurodiagnostic EEG technologist was available for real-time monitoring during all shifts. The events were categorized into those detected immediately (eyes-on), later in the same shift (delayed) or identified on the subsequent shift (missed). Chi-square and Fisher's exact tests were used for statistical comparisons. In January 2017, there were 25 patients (117 days of monitoring) in the EMU and 54 inpatients (146 days of monitoring) on cEEG with 92 total events, (39% seizures). In June 2017, there were 30 patients (133 days of monitoring) in the EMU and 47 additional inpatients (80 days of monitoring) on cEEG with 110 total events, (39% seizures). The number of events identified in real time was low and did not significantly differ among shifts regardless of the availability of the monitoring technologist. Most events were identified at the time of subsequent EEG scanning by the EEG technologist. Partial staffing for continuous video EEG surveillance is insufficient to identify events in real time. EEG technologists are able to identify events during regular EEG scanning.

Heart rate and autonomic biomarkers distinguish convulsive epileptic vs. functional or dissociative seizures.

OBJECTIVE: 20-40% of individuals whose seizures are not controlled by anti-seizure medications exhibit manifestations comparable to epileptic seizures (ES), but there are no EEG correlates. These events are called functional or dissociative seizures (FDS). Due to limited access to EEG-monitoring and inconclusive results, we aimed to develop an alternative diagnostic tool that distinguishes ES vs. FDS. We evaluated the temporal evolution of ECG-based measures of autonomic function (heart rate variability, HRV) to determine whether they distinguish ES vs. FDS. METHODS: The prospective study includes patients admitted to the University of Rochester Epilepsy Monitoring Unit. Participants are 18-65 years old, without therapies or co-morbidities associated with altered autonomics. A habitual ES or FDS is recorded during admission. HRV analysis is performed to evaluate the temporal changes in autonomic function during the peri­ictal period (150-minutes each pre-/post-ictal). We determined if autonomic measures distinguish ES vs. FDS. RESULTS: The study includes 53 ES and 46 FDS. Temporal evolution of HR and autonomics significantly differ surrounding ES vs. FDS. The pre-to-post-ictal change (delta) in HR differs surrounding ES vs. FDS, stratified for convulsive and non-convulsive events. Post-ictal HR, total autonomic (SDNN & Total Power), vagal (RMSSD & HF), and baroreflex (LF) function differ for convulsive ES vs. convulsive FDS. HR distinguishes non-convulsive ES vs. non-convulsive FDS with ROC>0.7, sensitivity>70%, but specificity<50%. HR-delta and post-ictal HR, SDNN, RMSSD, LF, HF, and Total Power each distinguish convulsive ES vs. convulsive FDS (ROC, 0.83-0.98). Models with HR-delta and post-ictal HR provide the highest diagnostic accuracy for convulsive ES vs. convulsive FDS: 92% sensitivity, 94% specificity, ROC 0.99). SIGNIFICANCE: HR and HRV measures accurately distinguish convulsive, but not non-convulsive, events (ES vs. FDS). Results establish the framework for future studies to apply this diagnostic tool to more heterogeneous populations, and on out-of-hospital recordings, particularly for populations without access to epilepsy monitoring units.