Fast evaluation of interictal spikes in long‐term EEG by hyper‐clustering

Abstract

SUMMARYPurpose: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring. Methods: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a densitybased algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies. Key Findings: Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min. Significance: Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases.

EEG source imaging of brain states using spatiotemporal regression

“…Spontaneous activity can also be described by a limited number of distinct potential maps repeating in time (EEG microstates, Lehmann et al, 1998). Finally, pathological activities, such as epileptic discharges are characterized by focus-specific typical topographies (Michel et al, 1999;Scherg et al, 2012).…”

“…Spontaneous activity can also be described by a limited number of distinct potential maps repeating in time (EEG microstates, Lehmann et al, 1998). Finally, pathological activities, such as epileptic discharges are characterized by focus-specific typical topographies (Michel et al, 1999;Scherg et al, 2012).In the first stage of our method we estimate the time courses of the EEG topographies of interest, which describe the salient brain states characterizing the EEG data acquired. This set of EEG maps acts as a spatial filter of the EEG by selecting only the sources time-correlated with the chosen maps.…”

Computer assisted interpretation of the human EEG

“…By having an automated method search for areas of interest in the EEG and then presenting only these findings to the reviewer, the visual burden can be minimized and review times can be reduced significantly (Scherg et al, 2012;Anderson and Dooli le, 2010). In addition, consistent feedback from the automated methods and more rigid definitions can improve inter-rater reliability and allow for more consistent reports (Halford et al, 2011).…”

“…Additional methods a er these reviews include (Nonclercq et al, 2009(Nonclercq et al, , 2012Ji et al, 2011a;Scherg et al, 2012). Promising results have been achieved if we look at the performance criteria chosen, and indeed, a substantial amount of research has been done in automated spike detection.…”

“…These include statistical measures such as variance, kurtosis and skewness (Scherg et al, 2012;Stevenson et al, 2013), non-linear energy operators (Mukhopadhyay and Ray, 1998), small-world networks and functional connectivity (Stam et al, 2007;Bullmore and Sporns, 2009), synchrony (Lachaux et al, 1999;van Pu en, 2003), entropy (Stam, 2005;Kannathal et al, 2005), power ratios (Kurtz et al, 2009;Cloostermans et al, 2011), bi-spectral index (Sigl and Chamoun, 1994), and le -right symmetry . Despite the variety of complex features available, relatively simple measures can be used to describe many of the background properties of an EEG.…”