Seizure detection/What is a seizure and when to detect it?
What is a seizure and when to detect it?
Seizures are a symptom associated with abnormal electrical activity in the brain, sometimes described as an electrical storm in the brain or earthquake in the brain (Osorio et al. 2010). Unfortunately, answering these questions remains a challenge for purposes of seizure detection, since there is currently no objective definition of what constitutes a seizure. This significantly hinders the perfection of seizure detection, as it is obviously difficult to develop an algorithm to detect events with perfect precision when the events themselves aren’t objectively identifiable. Strategies to deal with this problem generally fall into two groups: (1) develop an SDA that detects those seizures that have certain characteristics in common, or (2) develop multi-faceted SDAs which attempt to detect all abnormal epileptiform activity present in brain signals, including not just unequivocal seizures but other relevant activity such as brief seizures, bursts, spike trains, and even single spikes, then correlate the occurrence of these detections with clinical events of interest in hopes of providing the most complete information possible about the brain system dynamics underlying seizures for that patient. The first approach has limitations in that seizures outside the target set may go undetected, while the second approach tends to detect both artifacts and other paroxysmal events (both epileptic and non-epileptic) that are not generally considered to be seizures. There is an ongoing debate regarding the dynamical relationship between spikes and seizures (Frei et al. 2010).
Even when the EEG exhibits changes that are considered to be an unequivocal seizure, significant differences (tens of seconds or more) often exist between experts as to when the electrographic onset (EO) and electrographic end (EE) should be marked. Whether or not seizures have clinical/behavioral manifestations and the time when such manifestations first begin, the so-called clinical onset (CO), are determined subjectively as well, since there isn’t always an experienced or trained observer there during the seizure to make the determination, patients are often unaware of their own seizures, and cognitive/functional testing is rarely administered during seizures (Osorio and Frei 2010). Inter-rater variability in EO and EE markings, and difficulty in determining which seizures are “clinical” complicate SDA development and performance assessment, since most applications desire detection of the signal changes that immediately follow EO (which can, e.g., be spike detection in some cases, or a shift in power spectral density in others, depending on what is marked as EO) and sometimes it is desirable to only detect clinical seizures, which may not be differentiable from other “subclinical seizures” (more properly termed “seizures not known to be clinical”) until well after EO. #F3-#F8 provide some illustrative examples.
One significant limitation in comparing expert visual analysis (EVA) scores with the results of an SDA is that EVA is typically retrospective – the electroencephalographer identifies that a seizure has occurred and then pages forward and backward through the signal display/printout to set the times at which they believe the event starts and ends. On the other hand, any SDA that operates in real-time is required to issue a decision at a particular time using only signal information available up to that point in time. While some SDAs have been developed that perform retrospectively to first determine a seizure’s presence and then back up to determine when it started (Chan et al. 2008), and these approaches have some utility for offline processing, the associated detection delay limits their usefulness for warning or closed-loop control applications.