Continuous energy variation during the seizure cycle: towards an on-line accumulated energy

Abstract

Objective-Increases in accumulated energy on intracranial EEG are associated with oncoming seizures in retrospective studies, supporting the idea that seizures are generated over time. Published seizure prediction methods require comparison to 'baseline' data, sleep staging, and selecting seizures that are not clustered closely in time. In this study, we attempt to remove these constraints by using a continuously adapting energy threshold, and to identify stereotyped energy variations through the seizure cycle (inter-, pre-, post-and ictal periods).Methods-Accumulated energy was approximated by using moving averages of signal energy, computed for window lengths of 1 and 20 min, and an adaptive decision threshold. Predictions occurred when energy within the shorter running window exceeded the decision threshold.Results-Predictions for time horizons of less than 3 h did not achieve statistical significance in the data sets analyzed that had an average inter-seizure interval ranging from 2.9 to 8.6 h. 51.6% of seizures across all patients exhibited stereotyped pre-ictal energy bursting and quiet periods.Conclusions-Accumulating energy alone is not sufficient for predicting seizures using a 20 min running baseline for comparison. Stereotyped energy patterns through the seizure cycle may provide clues to mechanisms underlying seizure generation.Significance-Energy-based seizure prediction will require fusion of multiple complimentary features and perhaps longer running averages to compensate for post-ictal and sleep-induced energy changes.

Epilepsy: Ever-changing states of cortical excitability

“…This is due to accessibility of data and the high fidelity of the recordings. These studies report changes in neuronal complexity and network activity on linear (Esteller et al, 2005) and non-linear EEG analysis (Martinerie et al, 1998;Litt et al, 2001;Iasemidis et al, 2005, Le Van Quyen, 2005, optical recording of intrinsic signals (Zhao et al, 2007) and a probing-stimulation technique (Kalitzin et al, 2005;Freestone et al, 2011). These changes last from seconds to hours prior to seizure onset.…”

Seizure anticipation: from algorithms to clinical practice

“…Note, however, that different definitions for false positive rates are in use. Several groups have determined false prediction rates by counting all false positives and dividing this number by the total duration of the analyzed recording [23,27,36,38,45 ], thereby ignoring that for each seizure contained in the recording, there is a preictal period (i.e. the prediction horizon) during which every alarm is counted as a true prediction and false predictions cannot occur by definition.…”

State-of-the-Art of Seizure Prediction

“…The first international workshop of this group was held in Bonn, Germany in 2002 and aimed at assessing the state of the field at that time by having each major group apply its methods to predict seizures from a shared set of continuous intracranial EEG data (Lehnertz and Litt, 2005). Findings obtained from applying a large number of analysis techniques are summarized in eight peerreviewed articles published together in the journal Clinical Neurophysiology Esteller et al, 2005;Harrison et al, 2005a;Iasemidis et al, 2005;Jerger et al, 2005;Jouny et al, 2005;Le Van Quyen et al, 2005;Mormann et al, 2005; see also Ebersole, 2005). The results of all these investigations were inconsistent and at times contradictory despite substantial efforts to provide uniform data in terms of disease type, conditions, and recordings.…”