Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings

Stereo-EEG features of temporal and frontal lobe seizures with loss of consciousness

The loss of consciousness (LOC) during seizures is one of the most striking features that significantly impact the quality of life, even though the neuronal network involved is not fully comprehended. We analyzed the intracerebral patterns in patients with focal drug-resistant epilepsy, both with and without LOC. We assessed the localization, lateralization, stereo electroencephalography (SEEG) patterns, seizure duration, and the quantification of contacts exhibiting electrical discharge. The degree of LOC was quantified using the Consciousness Seizure Scale. Thirteen patients (40 seizures) with focal drug-resistant epilepsy underwent SEEG. In cases of temporal lobe epilepsy (TLE, 6 patients and 15 seizures), LOC occurred more frequently in seizures with mesial rather than lateral temporal lobe onset. On the other hand, in cases of frontal lobe epilepsy (7 patients; 25 seizures), LOC was associated with pre-frontal onset, a higher number of contacts with epileptic discharge compared to the onset count and longer seizure durations. Our study revealed distinct characteristics during LOC depending on the epileptogenic zone. For temporal lobe seizures, LOC was associated with mesial seizure onset, whereas in frontal lobe epilepsy, seizure with LOC has a significant increase in contact showing epileptiform discharge and a pre-frontal onset. This phenomenon may be correlated with the broad neural network required to maintain consciousness, which can be affected in different ways, resulting in LOC.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.