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Applications of Machine Learning Classifiers in Epileptic Seizure Detection
Published in Ricardo A. Ramirez-Mendoza, Jorge de J. Lozoya-Santos, Ricardo Zavala-Yoé, Luz María Alonso-Valerdi, Ruben Morales-Menendez, Belinda Carrión, Pedro Ponce Cruz, Hugo G. Gonzalez-Hernandez, Biometry, 2022
Mohammad Kubeb Siddiqui, Ruben Morales-Menendez
In epileptic seizures, the term ictal also comes in the literature. It also means a seizure. The seizure has four main stages [99, 166, 536]; Pre-ictal means the time before the seizure. It can be seconds, minutes or days. The stage is not the same for every patient; it applies to those who do experience this stage. Not every patient experiences something different at this stage of a seizure; some of them who experience a pre-ictal stage, use it as an alert to prepare for the seizure or a safe place [536].Ictal means seizure. There will be changes in the patient’s body; the electrical storm in the patient’s brain comes to life. During this stage, an alteration found in cardiovascular metabolism and changes in the EEG signals can be noted, [153].Inter-ictal explains the time that elapses between seizures, [170, 496]. More than 50% of epileptic patients suffer from temporal lobe epilepsy; these result in emotional disturbances between seizures, including anxiety and depression.Post-ictal means post-attack stage [144]. The stage counts from minutes to an hour. It depends on the type, intensity, and duration of the seizure. Activities vary from person to person, and patients may experience various disorders, such as headache, vomiting, confusion, and loss of consciousness. In most cases, patients do not remember what happened to them during a seizure [270].
Detection of Epileptic Seizures from EEG Data
Published in Narayan Panigrahi, Saraju P. Mohanty, Brain Computer Interface, 2022
Narayan Panigrahi, Saraju P. Mohanty
Epilepsy is considered as a phenomena emanating from the temporal lobe of the brain and is diagnosed as temporal lobe epilepsy as the epileptogenic focus being hippocampal formation. A schematic of an intracranial electrode placement is shown in Figure 12.3(b). The depth electrode was implanted symmetrically into the hippocampal formations and the strip electrodes were implanted onto the lateral and basal regions of the neocortex (Figure 12.3(b)). The EEG segments selected from all the recording sites exhibit ictal activity. Each EEG segment is considered as a separate EEG signal, resulting in a sizable number of EEG data segments.
Feature ranking chi-square method to improve the epileptic seizure prediction by employing machine learning algorithms
Published in Waves in Random and Complex Media, 2023
Lal Hussain, Eatedal Alabdulkreem, Kashif Javed Lone, Fahd N. Al-Wesabi, Mohamed K. Nour, Anwer Mustafa Hilal, Radwa Marzouk, Shafqat Aziz
The Bonn EEG dataset is a widely used publicly available dataset of EEG signals for research in the field of seizure detection and epilepsy diagnosis. The dataset was created by the University of Bonn in Germany and consists of five sets of EEG recordings, each containing 100 single-channel EEG recordings sampled at 173.61 Hz with a duration of 23.6 s. The recordings were obtained from five subjects with different types of epilepsy, including temporal lobe epilepsy, frontal lobe epilepsy, and primary generalized epilepsy. Each recording is labeled as either containing a seizure or being an interictal recording (i.e. a recording obtained during a period of normal brain activity between seizures). The dataset also includes annotations of the onset and end times of the seizures. The EEG signals were recorded using a TMSi Porti system with a Ag/AgCl electrode attached to the scalp at the location of the Cz electrode according to the international 10–20 system. The signals were preprocessed to remove any artifacts and baseline drifts and were then downsampled to 173.61 Hz. The Bonn EEG dataset has been widely used in research on seizure detection, epilepsy diagnosis, and machine-learning techniques for EEG signal analysis. The dataset is freely available for download and can be used for non-commercial research purposes with proper attribution to the original authors.
Oleanolic acid suppresses pentylenetetrazole-induced seizure in vivo
Published in International Journal of Environmental Health Research, 2023
Canan Akünal Türel, Oruç Yunusoğlu
Epilepsy is a common disease of the nervous system. Although most epilepsy patients can obtain effective relief from seizures with antiepileptic drugs, 25% of patients develop drug-resistant epilepsy (Mehdizadeh et al. 2019; Löscher et al. 2020). The control of epilepsy, mainly in drug-resistant epilepsy, is difficult and can cause financial and social issues, and diminish quality of life, mood, and cognition of patients (Büget et al. 2016; Allahverdiyev et al. 2018; Löscher et al. 2020). Conventional antiepileptic pharmacological agents are the first choice for the treatment and control of epileptic seizures. More than 20 AED pharmacological agents approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA) worldwide have been shown to provide reasonable seizure suppression potential (Liu et al. 2017; Allahverdiyev et al. 2018; Löscher et al. 2020). Nevertheless, there remains a need for the development and research of new antiepileptic drugs. The most common type of intractable epilepsy is temporal lobe epilepsy. Although the pathogenesis of temporal lobe epilepsy has not yet been fully elucidated, the fundamental cause of epilepsy is abnormal neuronal function (Liu et al. 2017; Allahverdiyev et al. 2018). Therefore, there is a need for further exploration of more possible novel therapeutic options.
Detection of the change in characteristics of self-grooming by the neural network in the latent period of the Rat Kainate Epilepsy model
Published in SICE Journal of Control, Measurement, and System Integration, 2022
Hirofumi Arai, Masaya Shigemoto, Kiyohisa Natsume
Epilepsy is a chronic neurological disorder in which spontaneous seizures repeatedly occur. Human temporal lobe epilepsy (hTLE) is accompanied by severe motor seizures [1], which occur acutely immediately after brain injury and insult [2]. Subsequently, there is a seizure-free period during which the brain neuronal network is reorganized [3,4]; this period is known as the latent period. After this period, repetitive seizures occur during the chronic period [3,4], during which behavioural changes can be observed [5,6]. We assumed that behavioural changes may also be seen from the latent period. As human behaviour is complicated, we first studied the behavioural changes in a rodent model of epilepsy. Kainate (KA), a glutamate receptor agonist, induces epilepsy in rats [7]. The development of this model was similar to that of hTLE [7] and the model also has a latent period. Thus, we adopted the rat KA model to record behavioural changes during the latent period [7].