Idiopathic generalized epilepsies (IGEs) are a subgroup of genetic generalized epilepsies (GGEs), composed of four syndromes: childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), and epilepsy with generalized tonic-clonic seizures alone (GTCA) (Hirsch et al., 2022). Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband.

The detector can analyze a real-time data stream. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. However, the overlap of the segments classified as ictal with seizures was only 83%. Using 19 channels (10-20 setup), we identified 99.2% of absences. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. Such seizures are the most compelling examples of pathological neuronal hypersynchrony.

Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies.

Absence seizure without eeg changes