Genetic and practical swarm optimisation algorithms for patient-specific seizure detection systems

Ammar, Sabrina; Trigui, Omar; Sarwat, Mohamed

doi:10.1109/atsip.2018.8364465

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…22 files among them 12 presenting seizures are available. The number of seizure segments is equal to 16 with duration equal to 511 sec [7] In typical scalp EEG, the recording is obtain by placing electrodes on scalp with a conductive gel. Some systems use caps into which electrodes are set; this is specific familiar when high-density arrays of electrodes are need [4].…”

Section: Proposed Work a Methodology And Implementationmentioning

confidence: 99%

Detection and Classification Epileptic Seizure

Kore¹

2019

IJRASET

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In this paper detection of epileptic seizure and non-seizure patient detected using the electroencephalogram (EEG). EEG of human brain is obtained by recording of EEG signal it is related to the medical field specialty, diagnosis of brain and brain related disease Proposed method based on wavelet coefficients types such as DWT, CWT, SWT (stationary wavelet transform) being translational invariant.in wavelet coefficient we use different properties. wavelet transform used for feature extraction and classifier used are support vector machine. Different artefact are removed using ICA.EEG advantageous for neurological disease if the uses of more feature extraction accuracy is better and also error rate is low.

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Section: Proposed Work a Methodology And Implementationmentioning

confidence: 99%

Detection and Classification Epileptic Seizure

Kore¹

2019

IJRASET

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“…Nature has always been a great source of inspiration to all mankind. In GA there is large search space which is very populated and has so many possible solutions to the given problem [13]. These solutions undergo many processes to produce new offspring with the help of chromosome and this process is repeated over again and again until 'fit' particle is found.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

Kochhar

Garg

2018

Int. J. Onl. Eng.

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<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

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“…The CHB-MIT dataset was used and a classification accuracy of 96.79 % was achieved. Ammar et al [8] used a particle swarm genetic algorithm for a patient specific seizure classification system. This classification model used seizure and nonseizure as the annotations.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

Davidson

McCallan

et al. 2022

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

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Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonicclonic models. This study proposes a method that utilises unique features with a novel parallel classifier -Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).

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Genetic and practical swarm optimisation algorithms for patient-specific seizure detection systems

Cited by 3 publications

References 9 publications

Detection and Classification Epileptic Seizure

Detection and Classification Epileptic Seizure

Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

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