Detection of preictal state in epileptic seizures using ensemble classifier

2021 ◽  
pp. 106818
Author(s):  
Syed Muhammad Usman ◽  
Shehzad Khalid ◽  
Sohail Jabbar ◽  
Sadaf Bashir
2016 ◽  
Vol 27 (01) ◽  
pp. 1650046 ◽  
Author(s):  
Yogatheesan Varatharajah ◽  
Ravishankar K. Iyer ◽  
Brent M. Berry ◽  
Gregory A. Worrell ◽  
Benjamin H. Brinkmann

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroaki Hashimoto ◽  
Hui Ming Khoo ◽  
Takufumi Yanagisawa ◽  
Naoki Tani ◽  
Satoru Oshino ◽  
...  

AbstractInfraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states.


2019 ◽  
Author(s):  
Carmen Diaz Verdugo ◽  
Sverre Myren-Svelstad ◽  
Celine Deneubourg ◽  
Robbrecht Pelgrims ◽  
Akira Muto ◽  
...  

SUMMARYBrain activity and connectivity alter drastically during epileptic seizures. Throughout this transition, brain networks shift from a balanced resting state to a hyperactive and hypersynchronous state, spreading across the brain. It is, however, less clear which mechanisms underlie these state transitions. By studying neuronal and glial activity across the zebrafish brain, we observed striking differences between these networks. During the preictal period, neurons displayed a small increase in synchronous activity only locally, while the entire glial network was highly active and strongly synchronized across large distances. We observed that the transition from a preictal state to a generalized seizure leads to an abrupt increase in neuronal activity and connectivity, which is accompanied by a strong functional coupling between glial and neuronal networks. Optogenetic activation of glia induced strong and transient burst of neuronal activity, emphasizing a potential role for glia-neuron connections in the generation of epileptic seizures.


Author(s):  
V. Pelliccia ◽  
C. Pizzanelli ◽  
S. Pini ◽  
P. Malacarne ◽  
U. Bonuccelli

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.


2003 ◽  
Author(s):  
A. Prasad ◽  
K. Narayanan ◽  
K. Tsakalis ◽  
L. Iasemidis

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