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 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 Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings

2012 ◽  
Vol 108 (1) ◽  
pp. 234-249 ◽  
Author(s):  
S. Boudet ◽  
L. Peyrodie ◽  
G. Forzy ◽  
A. Pinti ◽  
H. Toumi ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Optimal Projection ◽  
Long Duration ◽  
Eeg Recordings

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 A Primer on Hyperdimensional Computing for iEEG Seizure Detection

2021 ◽  
Vol 12 ◽  
Author(s):  
Kaspar A. Schindler ◽  
Abbas Rahimi
Keyword(s):  
Energy Efficiency ◽  
High Sensitivity ◽  
Epileptic Seizures ◽  
High Specificity ◽  
High Energy ◽  
Implantable Devices ◽  
Brain Signals ◽  
Eeg Recordings ◽  
Compact Design ◽  
Electroencephalogram Eeg

A central challenge in today's care of epilepsy patients is that the disease dynamics are severely under-sampled in the currently typical setting with appointment-based clinical and electroencephalographic examinations. Implantable devices to monitor electrical brain signals and to detect epileptic seizures may significantly improve this situation and may inform personalized treatment on an unprecedented scale. These implantable devices should be optimized for energy efficiency and compact design. Energy efficiency will ease their maintenance by reducing the time of recharging, or by increasing the lifetime of their batteries. Biological nervous systems use an extremely small amount of energy for information processing. In recent years, a number of methods, often collectively referred to as brain-inspired computing, have also been developed to improve computation in non-biological hardware. Here, we give an overview of one of these methods, which has in particular been inspired by the very size of brains' circuits and termed hyperdimensional computing. Using a tutorial style, we set out to explain the key concepts of hyperdimensional computing including very high-dimensional binary vectors, the operations used to combine and manipulate these vectors, and the crucial characteristics of the mathematical space they inhabit. We then demonstrate step-by-step how hyperdimensional computing can be used to detect epileptic seizures from intracranial electroencephalogram (EEG) recordings with high energy efficiency, high specificity, and high sensitivity. We conclude by describing potential future clinical applications of hyperdimensional computing for the analysis of EEG and non-EEG digital biomarkers.

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification

2016 ◽  
Vol 27 (02) ◽  
pp. 1650032 ◽  
Author(s):  
Yu Zhang ◽  
Yu Wang ◽  
Jing Jin ◽  
Xingyu Wang
Keyword(s):  
Motor Imagery ◽  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
Selection Of

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.

Realization of Enhanced LMS Filter Using Carry Skip Adder And Quantum Dot Cellular Automation In VLSI For Removing Impulse Noises In Medical Image

2019 ◽  
Vol 14 ◽  
Author(s):  
K.R. Shankarkumar ◽  
Gokul Kumar
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Pass Band ◽  
Least Mean Square ◽  
Low Pass ◽  
Parallel Prefix ◽  
Loop Feedback ◽  
Band Pass ◽  
The Difference ◽  
Prefix Adder

: Filtering is an important step in the field of image processing to suppress the required parts or to remove any artifacts present in it. There are different types of filters like low pass, high pass, Band pass, IIR, FIR and adaptive filtering etc.., in these filters adaptive filters is an important filter because it is used to remove the noisy signal and images. Least Mean Square filter is a type of an adaptive filtering which is used to remove the noises present in the medical images. The working of LMS is based on the minimization of the difference between the error images using a closed loop feedback. Therefore presented technique called as Q-CSKA. Here the CSKA performs its operation in stages which is based on the nucleus stage. In the traditional CSKA the nucleus stage is depend on the parallel prefix adder in this work it is replaced by the QCA adder. The QCA adder utilizes the less area compared to PPA and it can be realized in Nanometer range also. For multiplexers, And OR Invert, OR and Invert logic is used to reduce the area and delay. Due to these advantages of the QCA, AOI-OAI logic the proposed method outperformed the LMS implementation in area, power, and accuracy and delay, this based five type image noise of medical pictures related to the best technique is out comes. It helps to medicinal practitioner to resolve the symptoms of patient with ease.

Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles!

2006 ◽  
Vol 15 (5) ◽  
pp. 500-514 ◽  
Author(s):  
Robert Leeb ◽  
Claudia Keinrath ◽  
Doron Friedman ◽  
Christoph Guger ◽  
Reinhold Scherer ◽  
...  
Keyword(s):  
Communication Channel ◽  
Muscular Activity ◽  
Proof Of Concept ◽  
Closed Loop System ◽  
Head Mounted Display ◽  
Eeg Recordings ◽  
Sensorimotor Rhythms ◽  
Electroencephalogram Eeg ◽  
Healthy Participants

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

scholarly journals Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

2020 ◽  
Vol 10 (1) ◽  
pp. 89-93
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
Severe Damage ◽  
Tree Model ◽  
Data Set ◽  
Feature Extraction Method ◽  
Precautionary Measures ◽  
Detection And Diagnosis ◽  
Electroencephalogram Eeg ◽  
Mean Variance

Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage

Anticipation of epileptic seizures from standard EEG recordings

The Lancet ◽  
2001 ◽  
Vol 357 (9251) ◽  
pp. 183-188 ◽  
Author(s):  
Michel Le Van Quyen ◽  
Jacques Martinerie ◽  
Vincent Navarro ◽  
Paul Boon ◽  
Michel D'Havé ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Eeg Recordings

scholarly journals Spectrum of Nonepileptic Paroxysmal Events in Children from Southern India

2019 ◽  
Vol 10 (04) ◽  
pp. 608-612
Author(s):  
Vykuntaraju K Gowda ◽  
Raghavendraswami Amoghimath ◽  
Naveen Benakappa ◽  
Sanjay K Shivappa
Keyword(s):  
Children And Adolescents ◽  
Age Of Onset ◽  
Age Groups ◽  
Epileptic Seizures ◽  
Breath Holding ◽  
Home Video ◽  
Psychogenic Nonepileptic Seizures ◽  
Wrong Diagnosis ◽  
Nonepileptic Seizures ◽  
Eeg Recordings

Abstract Background Nonepileptic paroxysmal events (NEPEs) present with episodes similar to epileptic seizures but without abnormal electrical discharge on electroencephalogram (EEG). NEPEs are commonly misdiagnosed as epilepsy. Epilepsy is diagnosed on the basis of a detailed history and examination. Emphasis during history to rule out the possibility of NEPE is important. The wrong diagnosis of epilepsy can lead to physical, psychological, and financial harm to the child and the family. Hence, this study was planned. Objective The objective of the study is to evaluate clinical profile, frequency, and spectrum of NEPE in children. Materials and Methods This is a prospective observational study. Patients with NEPE between January 2014 and August 2016 aged < 18 years were enrolled. NEPEs were diagnosed on the basis of history, home video, and EEG recordings. Patients were divided into different categories according to age, specific type of disorder, and system responsible. Patients were followed for their NEPE frequency and outcome. Results A total of 3,660 children presented with paroxysmal events; of them 8% were diagnosed with NEPE. Patients diagnosed with NEPE were classified into three age groups on the basis of their age of onset of symptom; of the total 285 patients, there were 2 neonates (0.7%), 160 infants (56%), and 123 children and adolescents (43.1%). Fifty-eight percent patients were boys. The most common diagnoses were breath-holding spells 113 (39%), followed by syncope 38 (13.3%) and psychogenic nonepileptic seizures 37 (12.9%). About 9 and 5% of patients had concomitant epilepsy and developmental delay, respectively. Conclusions NEPEs account for 8% of paroxysmal events. Most common NEPEs were breath-holding spells among infants and syncope and “psychogenic nonepileptic seizures” in children and adolescents.

Sign in / Sign up

Export Citation Format

Share Document