Detection of Interictal Epileptiform Discharges Using Signal Envelope Distribution Modelling: Application to Epileptic and Non-Epileptic Intracranial Recordings

Abstract

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector's specificity and having important implications for future development of spike detectors in general.

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers

“…We also note the lack of comparable study to provide us with a suitable standard. As an alternative comparison, for IED detection algorithm validation, we find sample sizes ranging from 279 to 6534 IEDs (Dümpelmann and Elger, 1999, Barkmeier et al, 2012, Gaspard et al, 2014, Janca et al, 2015) however, detection is a much less complex and arduous task than IED classification (Gotman, 1999, James et al, 1999). Furthermore, fatigue and error of the EEG reviewer can be a source of error in IED marking (Barkmeier et al, 2012) which may also result in erroneous IED classification.…”

A retrospective glance at automatic detection of epileptic spike in electroencephalogram

“…Janca's study examines the source of false detections using principal component analysis (PCA) and finds that almost half of the false detection could be atypical spikes (62). In addition, beta and mu rhythmic activities form a source of false detections due to their overlapping in frequency with spikes.…”

“…In addition, beta and mu rhythmic activities form a source of false detections due to their overlapping in frequency with spikes. Non-epileptic sharp transients constitute the majority of the rest false detections, which tend to have significantly smaller spatial distribution than spikes (62). Artifacts are the main source of false positive.…”

“…Artifacts are the main source of false positive. The signal-to-noise ratio (SNR) is determined as the ratio between the power of the spike and that of the background activity, thus removal of artifacts are crucial (62). Theoretically, reasonably accurate spike detection can be obtained when the EEG is free from artifacts (67).…”

“…Using bipolar montage also helps eliminate artifacts common to adjacent electrodes. Clustering, expert system, dipole sources focusing are also proposed (30,46,62). There is no consistent opinion about which is the most effective artifact removal method.…”