Comparative investigation of machine learning algorithms for detection of epileptic seizures

2021 ◽  
pp. 1-11
Author(s):  
Akash Sharma ◽  
Neeraj Kumar ◽  
Ayush Kumar ◽  
Karan Dikshit ◽  
Kusum Tharani ◽  
...  
Keyword(s):  
Epileptic Seizure ◽  
Preventive Treatment ◽  
Epileptic Seizures ◽  
Comparative Investigation ◽  
Machine Learning Algorithms ◽  
Average Value ◽  
Prediction Time ◽  
The Right ◽  
Electroencephalogram Eeg ◽  
The Brain

In modern day Psychiatric analysis, Epileptic Seizures are considered as one of the most dreadful disorders of the human brain that drastically affects the neurological activity of the brain for a short duration of time. Thus, seizure detection before its actual occurrence is quintessential to ensure that the right kind of preventive treatment is given to the patient. The predictive analysis is carried out in the preictal state of the Epileptic Seizure that corresponds to the state that commences a couple of minutes before the onset of the seizure. In this paper, the average value of prediction time is restricted to 23.4 minutes for a total of 23 subjects. This paper intends to compare the accuracy of three different predictive models, namely – Logistic Regression, Decision Trees and XGBoost Classifier based on the study of Electroencephalogram (EEG) signals and determine which model has the highest rate of detection of Epileptic Seizure.

scholarly journals The State of the Art in Feature Extraction Methods for EEG Classification

2019 ◽  
Vol 3 (2) ◽  
pp. 16
Author(s):  
Hoger Mahmud Hussen
Keyword(s):  
Epileptic Seizure ◽  
Neurological Disease ◽  
State Of The Art ◽  
Epileptic Seizures ◽  
Extraction Methods ◽  
Eeg Classification ◽  
The World ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Eeg Features

Epileptic seizure is a neurological disease that is common around the world and there are many types (e.g. Focal aware seizures and atonic seizure) that are caused by synchronous or abnormal neuronal activity in the brain. A number of techniques are available to detect the brain activities that lead to Epileptic seizures; one of the most common one is Electroencephalogram (EEG) that uses visual scanning to measure brain activities generated by nerve cells in the cerebral cortex. The techniques make use of different features detected by EEG to decide on the occurrence and type of seizures. In this paper we review EEG features proposed by different researches for the purpose of Epileptic seizure detection, also analyze, and compare the performance of the proposed features.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure

2021 ◽  
Vol 17 (2) ◽  
pp. 109-113
Author(s):  
Ameen Omar Barja
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Epileptic Seizure ◽  
Essential Role ◽  
Clinical Neurophysiology ◽  
Alternative Form ◽  
Eeg Signals ◽  
Seizure Disorders ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.

Epilepsy in later childhood and adulthood

2010 ◽  
pp. 4810-4826
Author(s):  
G.D. Perkin ◽  
M.R. Johnson
Keyword(s):  
Brain Imaging ◽  
Neuronal Activity ◽  
Case History ◽  
Epileptic Seizure ◽  
Neurological Disease ◽  
Epileptic Seizures ◽  
Social Consequences ◽  
Definition Of ◽  
The Brain ◽  
Structural Brain

Case History—A 33 yr old woman, known to have epilepsy, now presenting with odd behaviour. An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is defined as a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiological, cognitive, psychological, and social consequences of this condition. The definition of epilepsy requires the occurrence of at least one epileptic seizure and evidence for an enduring alteration in the brain that increases the likelihood of future seizures such as an ‘epileptiform’ EEG abnormality, an appropriate lesion on structural brain imaging (CT or MRI), or the presence of recurrent (two or more) seizures. Epilepsy is a common, serious neurological disease, with prevalence 1% and a cumulative lifetime risk of 5%....

Epileptic Seizure Detection From EEG Signals Using Bagged Ensemble Approach

2020 ◽  
pp. 67-79
Author(s):  
Pradeep Singh ◽  
Sujith Kumar Appikatla
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
Ensemble Classifier ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Epileptic Seizure Detection ◽  
Electrical Pulses ◽  
Two Phases ◽  
The Brain

Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.

scholarly journals Low-frequency electroencephalogram oscillations govern left-eye lateralization during anti-predatory responses in the music frog

2020 ◽  
Vol 223 (21) ◽  
pp. jeb232637
Author(s):  
Jiangyan Shen ◽  
Ke Fang ◽  
Ping Liu ◽  
Yanzhu Fan ◽  
Jing Yang ◽  
...  
Keyword(s):  
Visual Field ◽  
Left Visual Field ◽  
Right Hemisphere ◽  
Brain Area ◽  
Power Spectra ◽  
Low Frequency ◽  
The Right ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Steady Velocity

ABSTRACTVisual lateralization is widespread for prey and anti-predation in numerous taxa. However, it is still unknown how the brain governs this asymmetry. In this study, we conducted behavioral and electrophysiological experiments to evaluate anti-predatory behaviors and dynamic brain activities in Emei music frogs (Nidirana daunchina), to explore the potential eye bias for anti-predation and the underlying neural mechanisms. To do this, predator stimuli (a model snake head and a leaf as a control) were moved around the subjects in clockwise and anti-clockwise directions at steady velocity. We counted the number of anti-predatory responses and measured electroencephalogram (EEG) power spectra for each band and brain area (telencephalon, diencephalon and mesencephalon). Our results showed that (1) no significant eye preferences could be found for the control (leaf); however, the laterality index was significantly lower than zero when the predator stimulus was moved anti-clockwise, suggesting that left-eye advantage exists in this species for anti-predation; (2) compared with no stimulus in the visual field, the power spectra of delta and alpha bands were significantly greater when the predator stimulus was moved into the left visual field anti-clockwise; and, (3) generally, the power spectra of each band in the right-hemisphere for the left visual field were higher than those in the left counterpart. These results support that the left eye mediates the monitoring of a predator in music frogs and lower-frequency EEG oscillations govern this visual lateralization.

scholarly journals Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Probabilistic Neural Network ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Classification Problems ◽  
Dynamic Features ◽  
Cepstral Coefficients ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
Electroencephalogram Eeg ◽  
The Brain

Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.

scholarly journals The fence post depth electrode technique to control both brain tumors and epileptic seizures in patients with brain tumor-related epilepsy

2019 ◽  
Vol 10 ◽  
pp. 187 ◽  
Author(s):  
Yosuke Masuda ◽  
Ayataka Fujimoto ◽  
Mitsuyo Nishimura ◽  
Keishiro Sato ◽  
Hideo Enoki ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Seizure Onset ◽  
Tumor Removal ◽  
Depth Electrodes ◽  
Fence Post ◽  
Group 2 ◽  
The Brain ◽  
Group 1

Background: To control brain tumor-related epilepsy (BTRE), both epileptological and neuro-oncological approaches are required. We hypothesized that using depth electrodes (DEs) as fence post catheters, we could detect the area of epileptic seizure onset and achieve both brain tumor removal and epileptic seizure control. Methods: Between August 2009 and April 2018, we performed brain tumor removal for 27 patients with BTRE. Patients who underwent lesionectomy without DEs were classified into Group 1 (13 patients) and patients who underwent the fence post DE technique were classified into Group 2 (14 patients). Results: The patients were 15 women and 12 men (mean age, 28.1 years; median age 21 years; range, 5–68 years). The brain tumor was resected to a greater extent in Group 2 than Group 1 (P < 0.001). Shallower contacts showed more epileptogenicity than deeper contacts (P < 0.001). Group 2 showed better epilepsy surgical outcomes than Group 1 (P = 0.041). Conclusion: Using DEs as fence post catheters, we detected the area of epileptic seizure onset and controlled epileptic seizures. Simultaneously, we removed the brain tumor to a greater extent with fence post DEs than without.

scholarly journals Limbic Encephalitis Associated with COVID-19

Encyclopedia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 26-35
Author(s):  
Natalia A. Shnayder ◽  
Timur K. Sirbiladze ◽  
Irina V. Demko ◽  
Marina M. Petrova ◽  
Regina F. Nasyrova
Keyword(s):  
Memory Impairment ◽  
Limbic Encephalitis ◽  
Cognitive Disorders ◽  
Epileptic Seizures ◽  
Long Term Memory ◽  
Short Term ◽  
Magnetic Resonance Imaging Mri ◽  
Electroencephalogram Eeg ◽  
The Brain

Limbic encephalitis (LE) is an inflammatory disease of the brain, in which lesion is anatomically limited in structures of the limbic system. In some cases, LE can start with symptoms of limbic dysfunction with further involvement of other regions of the brain. Classic LE syndrome includes such symptoms as the development of personality disorders, depression, sleep disorders, epileptic seizures, hallucinations and cognitive disorders (short-term and long-term memory impairment). The information of clinical examination, electroencephalogram (EEG), magnetic resonance imaging (MRI) and cerebrospinal fluid studies (CSF) suggest the diagnosis of LE in most patients with Coronavirus Disease 2019 (COVID-19).

scholarly journals Case Report: Anti-LGI1 Limbic Encephalitis Associated With Anti-thyroid Autoantibodies

2021 ◽  
Vol 11 ◽  
Author(s):  
Ricardo Otiniano-Sifuentes ◽  
Anali Cuba Antezana ◽  
Walter F. De La Cruz Ramirez ◽  
Kevin Pacheco-Barrios ◽  
Darwin A. Segura Chavez
Keyword(s):  
Limbic Encephalitis ◽  
Brain Magnetic Resonance Imaging ◽  
Autoimmune Encephalitis ◽  
Epileptic Seizures ◽  
Clonic Seizure ◽  
Thyroglobulin Antibodies ◽  
Temporal Lobes ◽  
Important Differential Diagnosis ◽  
The Right ◽  
The Brain

Anti-LGI1 encephalitis is an autoimmune encephalitis with antibodies against leucine-rich glioma-inactivated 1 (LGI1), first described in 2010. It is a non-frequent and poorly understood entity that represents the second most frequent cause of autoimmune encephalitis. This entity is characterized by the presence of limbic encephalitis, hyponatremia, and faciobrachial dystonic seizures. Herein, we present the case of a male patient with an onset of epileptic seizures (generalized tonic-clonic seizure), and involuntary dystonic movements that affect the right side of his face and right upper limb associated with mental disorder, and affectation of higher functions. The electroencephalogram showed continuous generalized slowing of the background activity. The brain magnetic resonance imaging showed signal hyperintensity at the level of both mesial temporal lobes and hippocampi and in the head of the right caudate nucleus. Anti-thyroglobulin antibodies were positive, and he was initially diagnosed as Hashimoto's encephalopathy (HE). However, the response to corticosteroids was not completed as it is usually observed in HE. For that, antibodies for autoimmune encephalitis were tested, and the anti-LGI1 antibodies were positive in serum and cerebrospinal fluid. HE is an important differential diagnosis to consider. Furthermore, the presence of Anti-thyroglobulin antibodies should not be taken as the definitive diagnostic criteria, since these antibodies could be associated with other autoimmune encephalopathies, which include in addition to anti-LGI1, anti-NMDA and anti-Caspr2.

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