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 A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Epileptic Seizures ◽  
Seizure Detection ◽  
New Approach ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
The Time Domain ◽  
Teager Energy ◽  
The Brain

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

scholarly journals Teager Energy Based Filter-Bank Cepstra in EEG Classification for Seizure Detection Using Radial Basis Function Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Filter Bank ◽  
Seizure Detection ◽  
World Population ◽  
Classification Problems ◽  
Dynamic Features ◽  
Radial Basis ◽  
Teager Energy

About 1–3% of the world population suffers from epilepsy. Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalograph (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through static and dynamic features derived from three Teager energy based filter-bank cepstra (TE-FB-CEPs). We compared the performance of linear, logarithmic, and Mel frequency scale TE-FB-CEPs using radial basis function neural network in general epileptic seizure detection. The comparison is tried on eight different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In a previous study, using traditional cepstrum on the same database, we had found that the composite vectors showed a degraded performance in seizure detection. In this study, however, irrespective of frequency scaling used, it is found that the composite vectors of TE-FB-CEPs maintain excellent overall accuracy in all the eight classification problems.

Automatic epileptic seizure detection using LSTM networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kishori Sudhir Shekokar ◽  
Shweta Dour
Keyword(s):  
Short Term Memory ◽  
Detection System ◽  
Research Work ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Content Type ◽  
Memory Network ◽  
The Common ◽  
Long Short Term Memory ◽  
The Brain

Purpose The purpose of this work is to make a computer aided detection system for epileptic seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more unprovoked seizures. The common and significant tool for aiding in the identification of epilepsy is Electroencephalography (EEG). The EEG signals contain information about the electrical activity of the brain. Conventionally, clinicians study the EEG waveforms manually to detect epileptic abnormalities, which is very time-consuming and error-prone. Design/methodology/approach The authors have presented a three-layer long short-term memory network for the detection of epileptic seizures. Findings The network classifies between seizure and non-seizure with 99.5% accuracy only in 30 epochs. This makes the proposed methodology useful for real-time seizure detection. Originality/value This research work is original and not plagiarized.

Visual Imagery Classification Using Shapelets of EEG Signals

2012 ◽  
Author(s):  
Stewart Contreras ◽  
V. Sundararajan
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Signal To Noise Ratio ◽  
Event Related Potential ◽  
Eeg Signals ◽  
Classification Rate ◽  
Average Classification Accuracy ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.

scholarly journals Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Samuel Boudet ◽  
Laurent Peyrodie ◽  
William Szurhaj ◽  
Nicolas Bolo ◽  
Antonio Pinti ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Epileptic Seizures ◽  
Step Method ◽  
Common Spatial Pattern ◽  
Optimal Projection ◽  
Filtering Rate ◽  
Low Pass ◽  
Eeg Recordings ◽  
Blind Comparison ◽  
Electroencephalogram Eeg

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

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).

Epileptic Seizure Detection and Classification Using Machine Learning

2019 ◽  
pp. 152-164
Author(s):  
Rekh Ram Janghel ◽  
Yogesh Kumar Rathore ◽  
Gautam Tatiparti
Keyword(s):  
Brain Activity ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
The United States ◽  
Normal Brain ◽  
Great Success ◽  
Ann Models ◽  
Recognition Systems ◽  
The Brain

Epilepsy is a brain ailment identified by unpredictable interruptions of normal brain activity. Around 1% of mankind experience epileptic seizures. Around 10% of the United States population experiences at least a single seizure in their life. Epilepsy is distinguished by the tendency of the brain to generate unexpected bursts of unusual electrical activity that disrupts the normal functioning of the brain. As seizures usually occur rarely and are unforeseeable, seizure recognition systems are recommended for seizure detection during long-term electroencephalography (EEG). In this chapter, ANN models, namely, BPA, RNN, CL, PNN, and LVQ, have been implemented. A prominent dataset was employed to assess the proposed method. The proposed method is capable of achieving an accuracy of 97.5%; the high accuracy obtained has confirmed the great success of the method.

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.

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