False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis

Abstract

Objective: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges. Approach: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges. To reduce the false alarms caused by abnormal background activity, we used morphological features extracted by visibility graph methods in addition to time-frequency features. To reduce the false alarms caused by over-interpreting short ictal discharges and interictal discharges, we created two synthetic classes-'Suspected Non-ictal' and 'Suspected Ictal'-based on the misclassified categories and constructed a synthetic 4-class dataset combining the standard two classes-'Non-ictal' and 'Ictal'-to train a 4-class classifier. Precision-recall curves were used to compare our proposed 4-class classification model and the standard 2-class classification model with or without the morphological features in the leave-one-out cross validation stage. The sensitivity and precision were primarily used as performance metrics for the detection of a seizure event. Main results: The 4-class classification model improved the performance of the standard 2-class model, in particular increasing the precision by 15% at an 80% sensitivity level when only time-frequency features were used. Using the morphological features, the 4-class classification model achieved the best performances: a sensitivity of 93% ± 12% and a precision of 55% ± 30% in the group level. 100% accuracy was reached in a participant's 4.3-hour recording with 5 ictal discharges. Significance: False alarms in the NCSE detection were remarkably reduced using the morphological features and the proposed 4-class classification model.

Pitfalls in EEG analysis in patients with non-convulsive status epilepticus

“…In automated analysis, slow wave activities and ictal discharges could be confused because their frequency bands are similar. An advanced signal processing technique, such as extracting features in the morphological besides the time-frequency domain features 24 , may be helpful in their distinction in future studies.…”

Novel electrode architecture for subgaleal electroencephalography: a feasibility study

“…39,40,54,55 Recording of non-convulsive seizures using non-EEG methods is challenging and remains a challenging task even for EEG-based solutions. 56 Time-limited and expensive hospital bedside physiological monitoring is the only tool available to clinicians at this time.…”

Novel Electrode Architecture for Subgaleal Electroencephalography: A Feasibility Study

“…Among many types of clinical epileptic seizures, non-convulsive seizures and non-convulsive status epilepticus are severe medical conditions that could lead to an emergency department and intensive care units (ICU) admission or death [54,39,40,55]. Recording of non-convulsive seizures using non-EEG methods is challenging and remains a challenging task even for EEG-based solutions [56]. Time-limited and expensive hospital bedside physiological monitoring is the only tool available to clinicians at this time.…”