Neonatal and Pediatric Electroencephalogram

Mudigoudar, Basanagoud; Fulton, Stephen; Weatherspoon, Sarah; Wheless, James W.

doi:10.1017/9781108754200.015

Cited by 1 publication

(1 citation statement)

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“…The participants included in this study were outpatients (adults and older children) with chronic seizures and brain development disorders; hence, the NCSE detection conclusions do not extend beyond this population. Future work should aim at infants and very young children whose NCSE presence is more common but EEG interpretation is challenging because of different seizure semiology and the corresponding changes of EEG signs in the rapid anatomic and physiological developing brain (Mudigoudar et al 2019). A small number of participants' recordings were investigated in our study, because the incidence of NCSE is relatively low among patients with epilepsy, for example, a study (Bhalla et al 2014) reported that only 6.7% of the 65 cases, who had epileptologist-confirmed status epilepticus, had NCSE.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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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Section: Discussionmentioning

confidence: 99%