Key result
A 4-class classification model using morphological features achieved a sensitivity of 93% and precision of 55% for detecting non-convulsive status epilepticus, reducing false alarms.
Why the study?
Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus.
Does a 4-class classification model using morphological features improve precision and sensitivity in detecting non-convulsive status epilepticus compared to a standard 2-class model?
Does a 4-class classification model using morphological features improve precision and sensitivity in detecting non-convulsive status epilepticus compared to a standard 2-class model?
A novel 4-class classification model incorporating morphological features significantly reduces false alarms in continuous EEG monitoring for non-convulsive status epilepticus.
May reduce false alarms in continuous EEG for NCSE; leaves open need for prospective validation before clinical adoption.
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.
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Wang et al. (2020) studied non-convulsive status epilepticus (NCSE) (n=10). 4-class classification model with morphological features vs. standard 2-class classification model was evaluated on sensitivity and precision for the detection of a seizure event. A 4-class classification model using morphological features achieved a sensitivity of 93% and precision of 55% for detecting non-convulsive status epilepticus, reducing false alarms.
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