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Automatic Epileptic Seizures Joint Detection Algorithm Based on Improved Multi-domain Feature of cEEG and Spike Feature of aEEG
2019
IEEE Access
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INDEX TERMS Seizure detection, multi-domain feature, spike detection, hybrid method. ...
In cEEG-based seizure detection algorithm, cEEG signals are divided into 5 s epoch with 4 s overlap and multi-domain features are extracted from each epoch. ...
In fact, the proposed feature extraction strategy from multi-domain based on multi-channel EEG signals can capture more discrimination information than a single EEG signal and the hybrid method can integrate ...
doi:10.1109/access.2019.2904949
fatcat:qofskpjpgrbzbpnjhxv62vdh4m
Automated Epileptic Seizure Detection Methods: A Review Study
[chapter]
2012
Epilepsy - Histological, Electroencephalographic and Psychological Aspects
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Other authors based their feature choice on morphological characteristics of epileptic EEG recordings. ...
Automated analysis of epileptic EEG recordings addresses two major problems: 1) inter-ictal spike detection or spike detection (section 2.1) and 2) epileptic seizure analysis. ...
doi:10.5772/31597
fatcat:s7fye47dqbgtzgmj723piyecce
AUTOMATIC DETECTION OF EPILEPSY EEG USING NEURAL NETWORKS
2012
International journal of computer and communication technology
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The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an ...
The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy has emerged in recent years.This paper proposes a neural-network-based automated epileptic EEG detection ...
In 1997, Qu and Gotman [16] proposed the use of the nearest-neighbor classifier on EEG features extracted in both the time and frequency domains to detect the onset of the epileptic seizures. ...
doi:10.47893/ijcct.2012.1149
fatcat:3cqv47keubdbbocs5ymivsued4
Implementation of Probabilistic Neural Network using Approximate Entr ximate Entropy to Detect Epileptic Seizur o Detect Epileptic Seizures
2012
Undergraduate Academic Research Journal
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This paper proposes one such automated epileptic seizure detection technique based on Probabilistic Neural Network (PNN) by using a time frequency domain characteristics of EEG signal called Approximate ...
Epileptic seizures detection is largely based on analysis of Electroencephalogram signals. The ambulatory EEG recordings generate very lengthy data which require a skilled and careful analysis. ...
As shown in Figure 1 the epileptic seizure detection system consists of three main modules: a feature extractor that generates a wavelet based feature from the EEG signals, feature selection that composes ...
doi:10.47893/uarj.2012.1003
fatcat:mtj74lkvjngatbewxebezyza4q
MSBE Analysis with Frequency Spectra for Automated Identification of Epileptic Seizure
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Objective-This study introduces a reliable automated seizure detection technique based on MSBE (Multi scale bubble entropy) and frequency spectral analysis. ...
Result-In this paper, an application of bubble entropy with different frequency parameter such as PPF and PSD is provided in order to access its stable and outstanding performance on epileptic seizer detection ...
This method makes use of five different attributes to detect the epileptic seizures, it considers two time-domain (spike rhythmicity and relative spike amplitude) and three frequency-domain features (dominant ...
doi:10.35940/ijitee.l3225.1081219
fatcat:42v74kmtgzexzhxnmjmdaaj7sa
A Survey on Different Techniques for Epilepsy Seizures Detection in EEG
2018
International Journal for Research in Applied Science and Engineering Technology
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An electroencephalogram (EEG) is a test out used to evaluate the electrical activity in the brain, and is widely used in the detection and study of epileptic seizures. ...
For this point, the occurrence components of the EEG are extracted by using the discrete wavelet transform (DWT) and parametric methods based on autoregressive (AR) model. ...
The plan for detecting epileptic and non epileptic spikes in EEG is based on a multi resolution, multi-level analysis and Artificial Neural Network (ANN) approach. ...
doi:10.22214/ijraset.2018.1306
fatcat:fgl67ofxvrf2ln3uzl3zwqj35e
New Feature Selection Method for Multi-channel EEG Epileptic Spike Detection System
2019
VNU Journal of Science Computer Science and Communication Engineering
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Tensor decomposition-based feature extraction has been proposed to facilitate automatic detection of EEG epileptic spikes. ...
Epilepsy is one of the most common and severe brain disorders. Electroencephalogram (EEG) is widely used in epilepsy diagnosis and treatment, with it the epileptic spikes can be observed. ...
Acknowledgments This work has been supported by VNU University of Engineering and Technology under project number CN18.15. ...
doi:10.25073/2588-1086/vnucsce.230
fatcat:pcb36c3ubnfl5ibk6zhqi76zoe
Improving classification of epileptic and non-epileptic EEG events by feature selection
2016
Neurocomputing
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Usually clinicians 18 differentiate between generalized epileptic seizures and PNES based on clinical features and video-19 EEG. ...
In this work, we investigate the use of machine learning techniques for automatic classification 20 of generalized epileptic and non-epileptic events based only on multi-channel EEG data. ...
paper, we investigated the problem of classification between epileptic and non-epileptic 4 events from multi-channel EEG data using a large number of time-domain and frequency domain 5 features. ...
doi:10.1016/j.neucom.2015.06.071
fatcat:wirttvjhivasxo2kz4zpsiu7uu
Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
2017
Entropy
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In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. ...
We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. ...
Acknowledgments: The work described in this paper is supported by three grants from the National Natural
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/e19060222
fatcat:vpcuor5t4fcefijdk4tfprtj5q
QuPWM: Feature Extraction Method for Epileptic Spike Classification
2020
IEEE journal of biomedical and health informatics
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Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. ...
The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. ...
INTRODUCTION
Fig. 1 : 1 Illustration of the brain abnormal activities for different types of epileptic seizures [built based on [5] [6]]. ...
doi:10.1109/jbhi.2020.2972286
pmid:32054592
fatcat:wvbwdllfdfbidplogou6lkbmce
A Multistage System for Automatic Detection of Epileptic Spikes
2018
REV Journal on Electronics and Communications
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A multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). ...
The system consists of four stages: preprocessing, feature extraction, classifier and expert system. ...
Hoang Cam Tu from Vietnam National Children's Hospital for recording and interpreting the EEG data. This work was supported by Project QG.10.40 from Vietnam National University, Hanoi. ...
doi:10.21553/rev-jec.166
fatcat:oknpfx45cffaxmlbaobz65z4gy
A Discriminative Approach To Automatic Seizure Detection In Multichannel Eeg Signals
2014
Zenodo
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Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014 ...
] which takes a wavelet transform approach to extract time-frequency based features from EEG bands to classify non-epileptic, interictal (periods of time between seizures) and ictal (periods of seizure ...
For example, the work of [3] evaluates classifiers for the detection of seizure onset, which means that the metric used to evaluate the performance is based on the positive detection of a seizure during ...
doi:10.5281/zenodo.44049
fatcat:qamvtsoiczh4lmhhquw6lbgxoe
Modified Time-Frequency Marginal Features for Detection of Seizures in Newborns
2022
Sensors
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The proposed set is based on the observation that EEG seizure signals appear either as a train of spikes or as a summation of frequency-modulated chirps with slow variation in the instantaneous frequency ...
Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording ...
signature of epileptic spikes or frequency-domain signatures of frequency-modulated chirps. ...
doi:10.3390/s22083036
pmid:35459022
pmcid:PMC9025536
fatcat:rgkeruoz2befjmbliloivodjzy
Epileptic Seizure Detection and Experimental Treatment: A Review
2020
Frontiers in Neurology
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One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. ...
Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the ...
(111) performed TFD feature extraction on multi-channel recordings for seizure detection in newborn EEG signals. ...
doi:10.3389/fneur.2020.00701
pmid:32849189
pmcid:PMC7396638
fatcat:dnnl7tyninc6zkqciwmuwo3esu
Implementation of High Performance EEG Based Seizure Detection And Analysis On Multicore Platform
2020
International Journal of Engineering Technology and Management Sciences
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The detection unit will detect the seizures based on feature extraction process once the seizure detection is done enables the analysis circuit that process the data based Uridva Triyabhakyam based 128 ...
There is a greater need to reduce the power consumption as well to increase the speed of EEG seizure detection system. ...
Seizure detection is done in Time domain, where energy parameter is used as seizure detection feature and analysis is done in Frequency domain. ...
doi:10.46647/ijetms.2020.v04i05.002
fatcat:qltrgs4465h3fknxfu4aktkopq
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