First do no harm: safety in the epilepsy monitoring unit

Assessment of an under-mattress sensor as a seizure detection tool in an adult epilepsy monitoring unit

OBJECTIVE

Because of SUDEP (Sudden and unexpected death in epilepsy) and other direct consequences of generalized tonic-clonic seizures, the use of efficient seizure detection tool may be helpful for patients, relatives and caregivers. We aimed to evaluate an under-mattress detection tool (EMFIT®) in real-life hospital conditions, in particular its sensitivity and false alarm rate (FAR), as well as its impact on patient care.

METHODS

We carried out a retrospective study on a cohort of patients with epilepsy admitted between September 2017 and June 2021 to Amiens University Hospital for a video-EEG of at least 24 h, during which at least one epileptic seizure was recorded. All video-EEGs records were analyzed visually in order to assess the sensitivity of the under-mattress tool (triggering of the alarm) and to classify the seizure type (convulsive/non convulsive). We also considered whether nurses intervened during the seizure, and the time of their intervention if applicable. An additional prospective survey was conducted over 272 days to analyze the FAR of the tool.

RESULTS

A total of 220 seizures were included in the study, from 55 patients, including 23 convulsive seizures from 15 patients and 197 non-convulsive seizures. Sensitivity for convulsive seizure detection was 69.6%. As expected, none of the non-convulsive seizures was detected. The false alarm rate was 0.007/day. Median trigger time was 74 s, decreasing to 5 s for generalized tonic-clonic seizure. The frequency of nurses' intervention during convulsive seizures was significantly greater in case of the alarm triggering (100% vs 57%, p<0.02).

SIGNIFICANCE

These results suggest that EMFIT® sensor is able to detect convulsive seizures with good sensitivity and low FAR, and allows caregivers to intervene more often in the event of a nocturnal seizure. This would be an interesting complementary tool to better secure the patients with epilepsy during hospitalization or at home.

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset

OBJECTIVE

To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity.

METHODS

Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations.

RESULTS

Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection.

CONCLUSIONS

Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns.

SIGNIFICANCE

This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Staff Response Times in the Epilepsy Monitoring Unit: A Study of Diurnal/Nocturnal Variability

Although inpatient epilepsy monitoring units (EMUs) are generally safe, seizures in this setting can still produce significant morbidity. The MORTEMUS (MORTality in Epilepsy Monitoring Unit Study) study revealed that the most feared consequence of an unattended seizure-sudden unexpected death in epilepsy (SUDEP)-does occur rarely in the EMU. Nearly all cases identified in that study occurred in the evening. The hypothesis for this study is that unwitnessed seizures would be more likely to occur during the night shift, and that response times to seizures would be slower at night, due to multiple variables. A retrospective video-EEG review of all seizures captured in our EMU during a 4-week period in 15 patients admitted was conducted. The time between seizure onset and the arrival of an attendant at the bedside was measured. There were 16 diurnal and 14 nocturnal seizures identified. The median response time during the day shift was approximately 22 ± 28 (0-80) seconds during the day shift, and 49 ± 93 (0-360) during the night shift (Mann-Whitney U test, P = 0.03). There were six seizures that were subclinical or showed subtle clinical signs (head turning or eyes opening), including one prolonged seizure lasting nearly 18 minutes, which all occurred in the evening, went unattended, and were excluded from the statistical analysis. These preliminary findings indicate a statistically significant delay in nursing response times to seizures in the EMU during the night shift. All unattended seizures occurred in the evening. More research is needed to study human factors, systems issues, or patient-related/physiological factors that slow response times.

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

OBJECTIVE

A method for automatic detection of epileptic seizures in long-term scalp-EEG recordings called EpiScan will be presented. EpiScan is used as alarm device to notify medical staff of epilepsy monitoring units (EMUs) in case of a seizure.

METHODS

A prospective multi-center study was performed in three EMUs including 205 patients. A comparison between EpiScan and the Persyst seizure detector on the prospective data will be presented. In addition, the detection results of EpiScan on retrospective EEG data of 310 patients and the public available CHB-MIT dataset will be shown.

RESULTS

A detection sensitivity of 81% was reached for unequivocal electrographic seizures with false alarm rate of only 7 per day. No statistical significant differences in the detection sensitivities could be found between the centers. The comparison to the Persyst seizure detector showed a lower false alarm rate of EpiScan but the difference was not of statistical significance.

CONCLUSIONS

The automatic seizure detection method EpiScan showed high sensitivity and low false alarm rate in a prospective multi-center study on a large number of patients.

SIGNIFICANCE

The application as seizure alarm device in EMUs becomes feasible and will raise the efficiency of video-EEG monitoring and the safety levels of patients.