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The present study proposes new method for epileptic seizure prediction based on heart rate variability (HRV) analysis and one-class support vector machines (SVM) technique. Methods: Excessive neural activity in preictal period affects not only brain activity, but also autonomic nervous system, that affects HRV. The proposed method distinguishes eight features, analyzes matrix of eigenvalues and eigenvectors,...
This work is devoted to the prediction of epileptic seizures using heart rate variability (HRV) characteristics. Several HRV features were extracted (statistical, spectral, histogram, polynomial approximation coefficients) for various durations of sliding time windows and various lengths of preictal intervals. The data from 14 subjects with generalized epileptic seizures was used. Support Vector Machine...
Interictal High Frequency Oscillations, (HFOs [30600 Hz]), recorded from intracerebral electroencephalo-graphy (iEEG) in epileptic brain, showed to be potential biomarkers of epilepsy. Hence, their automatic detection has become a subject of high interest. So far, all detection algorithms consisted of comparing HFOs energy, computed in bands of interest, to a threshold. In this paper, a sequential...
Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG...
One of the major disorders affecting the human brain is epilepsy and it impairs the daily lives of the patients. Epilepsy is caused by sudden and random incidence of seizures. One of the most powerful tool for diagnosing various neurological disorders including epilepsy is Electroencephalogram (EEG). Recognizing the people who have a disorder of the brain by means of inspection of EEG signals visually...
Epilepsy is the fourth most frequent neurological disorder. Epileptic seizures are the result of temporary electrical disturbances in the brain. This disorder can be diagnosed by electroencephalograms (EEG). Accordingly, data mining supported by machine learning (ML) methods can be used to find patterns in EEG and to build classifiers. However, the presence of physiological abnormalities is considered...
Pre-diagnosis of epileptic seizure using electroencephalogram (EEG) signals detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance. Recording and reviewing of entire length of EEG is used to analyse epileptic activity by an expert. The classical method thus being a tedious task many intelligent automatic seizure detection schemes have...
To control the coordination between various muscles and nerves in the human body, brain is utilized. Due to the sudden, unexpected and transient electrical disturbances of the brain, it results in an acute disease called epilepsy which is characterized by recurrent seizures leading to a lot of temporary changes in behaviour, perception, movement and health. Epilepsy occurs due to the rapid firing...
The main aim of this research is to find the feasibility of Linear Discriminant Analysis (LDA) in optimization of fuzzy outputs for epilepsy risk level classification from EEG signals. One of the prominent neurological disorders affecting the nervous system is epilepsy. Due to the hyperactivity of neurons in certain regions of the brain epileptic seizures occur. To classify the epilepsy risk levels...
In the arena of biomedical engineering, the classification and analysis of epilepsy from Electroencephalography (EEG) signals forms an important area of research. When the neurons get hyper excited, seizures occur causing a lot of inconvenience and trouble to the patient. For the study of the predominant abnormalities in the cerebral cortex of the brain, EEG is used widely. Due to the long nature...
As a result of sudden and excessive electrical discharges in a specific group of brain cells called neurons, epilepsy occurs and is usually for a brief period. It can occur in various parts of the brain and the patient can experience different symptoms depending on the occurrence of the excessive discharges. So the electrical impulses generated due to the nerve firing in the brain can be measured...
One of the age old neurological disorders found in human beings is epilepsy. It is witnessed by the abnormal electrical activities in the brain which causes recurring seizures, therefore the patient suffers from loss of consciousness. Due to the random nature of the seizures, the patients may not be aware of it and so it increases the risk of physical injury. Due to the disturbed brain activity, epileptic...
Epilepsy is the most common chronical disorder affecting nearly 50 million people worldwide and is associated with periodic loss of consciousness characterized by recurrent seizures with the abnormal electrical activity of the brain. According to Bangalore Urban Rural Neuro-epidemiological Survey (BURNS) approximately 51502 urban and 51055 rural people are affected with epilepsy in Bangalore. The...
This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic...
This paper proposes a novel patient-specific approach to channel selection and seizure detection based on estimating the histograms of multi-channel scalp electroencephalography (sEEG) signals. It consists of two main phases: training and testing. In the training phase, the signal is segmented into non-overlapping 10-second segments, with five histograms estimated for each segment. These histograms...
The most commonly used clinical tool for initial diagnosis of epilepsy is electroencephalogram (EEG). Recent advances in magnetoencephalography (MEG) technology provide a new source of information to analyze brain activities. In order to determine whether or not particular subjects' brain signals exhibit epileptic activities, epileptologists often spend considerable amount of time to review MEG recordings...
Epilepsy is an infirmity which affects the brain causing repeated seizures. An automatic novel method is used for analyzing the EEG signal and for detecting epileptic seizure activity. The proposed method is tested on a publicly available dataset and it uses two time domain features namely line length and energy. Classification algorithms-1) Quadratic discriminant analysis (QdA), 2) K-Nearest Neighbour...
Cyclic alternating patterns (CAPs) occur during normal sleep, but higher CAP rates are associated with abnormal conditions, such as epilepsy. Efficient automatic classification of CAP A-phase sub-types would be of remarkable importance for the consideration of CAP as a disease bio-marker. This paper reports a multi-step methodology for the classification of A-phases subtypes. The methodology encompasses:...
The classification of epileptic seizure events in EEG signals is an important problem in biomedical engineering. In this paper we propose a Bayesian classification method for multivariate EEG signals. The method is based on a multilevel 2D wavelet decomposition that captures the distribution of energy across the different brain rhythms and regions, coupled with a generalised Gaussian statistical representation...
Despite the fact that depressive disorders are the most common comorbidities among patients with epilepsy (PWEs), they often go unrecognized and untreated. The availability of validated screening instruments to detect depression in PWEs is limited. The aim of the present study was to validate the Hospital Anxiety and Depression Scale (HADS) in adult PWEs.A consecutive group of 118 outpatient PWEs...
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