Imagine a synchronized dance performance where all the dancers move in perfect harmony, but sometimes this harmony is disrupted. In absence seizures, the brain experiences a similar phenomenon of hyper-synchronized activity known as generalized spike-and-wave discharges (SWDs). Scientists have been investigating the possibility of using EEG phase synchronization to detect and quantify the disorganization of these seizures. However, they discovered that changes in EEG synchronization alone are not sufficient for accurate seizure detection. To overcome this challenge, a machine learning classifier was developed that combined the phase synchronization index and normalized amplitude as features. This classifier successfully identified 99.2% of absences but only had an 83% overlap with actual seizures. Interestingly, the analysis revealed that approximately half of the patients exhibited disorganized seizures. Further research is needed to understand how different seizure properties and clinical characteristics can distinguish between different types of absence epilepsies. To dive deeper into this fascinating study and understand the complexities of brain synchronization during absence seizures, check out the full article!

Absence seizures—generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. 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). 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. 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. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. 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. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.

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