Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units

Abstract

Due to increased awareness of the high prevalence of nonconvulsive seizures (NCSs) in critically ill patients, continuous EEG monitoring (cEEG) in ICUs is rapidly increasing in use. However, cEEG monitoring is labor intensive; manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of NCSs would greatly reduce the cost of cEEG and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed a mean sensitivity of 90.4% and a mean false detection rate of 0.066/h. The two commercial detection products performed with low sensitivities (12.9% and 10.1%) and false detection rates of 1.036/h and 0.013/h, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of NCSs. This study also suggests that currently available seizure detection software does not have sufficient performance for the detection of NCSs in critically ill patients.

Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part II

“…QEEG trends can aid in rapid identification and quantification of NCS, but may miss seizures, even when reviewed by experienced readers (Anderson and Wisneski 2008, Stewart, Otsubo et al 2010, Sackellares, Shiau et al 2011, Pensirikul, Beslow et al 2013).

Many seizures in critically ill patients contain rhythmic waveforms in the 2-6 or 6-14 Hz frequency ranges.

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“…Most automated seizure detection algorithms were developed for ictal patterns seen in patients with established epilepsy, and have not been validated in ICU populations with acute symptomatic seizures (Sackellares, Shiau et al 2011). Automated analysis of background patterns (e.g.…”

Continuous EEG monitoring in adults in the intensive care unit (ICU)

“…There is thus a real need to develop new, more adequate algorithms [63]. Power spectral density analysis or a-EEG cannot be used in an isolated manner to detect short-duration seizures or highly focal low-amplitude discharges that could be overlooked [68].…”

Quantified EEG for the Characterization of Epileptic Seizures versus Periodic Activity in Critically Ill Patients

“…In recent years, the development of mathematical analysis tools for bioelectric signals, commonly known by the acronym qEEG (quantified EEG), has introduced elements of objectivity into the analysis of EEG records [10]. In the ICU field, the qEEG has been applied to facilitate the interpretation of prolonged EEG recordings, as well as the identification of electrographic seizures [11][12][13][14].…”