Epileptic seizure detection using fuzzy-rules-based sub-band specific features and layered multi-class SVM

Ramakrishnan, S.; Murugavel, A. S. Muthanantha

doi:10.1007/s10044-018-0691-6

Cited by 33 publications

(8 citation statements)

References 30 publications

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“…Further, they used the Support Vector Machine-Error-Correcting Output Codes (SVM-ECOC) to train the classification algorithm, and the improvement in classification accuracy had been obtained. Ramakrishnan and Murugavel (2019) proposed a new seizure detection model using layered directed acyclic graph SVM (LDAG-SVM), which improved classification accuracy and reduced detection time compared to existing methods. After performing DWT, Chen et al (2019) extracted the non-linear features of each sub-band and inputted them into six different classifiers for training.…”

Section: Classificationmentioning

confidence: 99%

“…Makaram et al (2020) extracted the time domain characteristics and signal complexity. Further, they used the Support Vector Machine-Error-Correcting Output Codes (SVM-ECOC) to train the classification algorithm, and the improvement in classification accuracy had been obtained Ramakrishnan and Murugavel (2019). proposed a new seizure detection model using layered directed acyclic graph SVM (LDAG-SVM), which improved classification accuracy and reduced detection time compared to existing methods.…”

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confidence: 99%

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Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

Xiao

et al. 2021

Front. Syst. Neurosci.

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Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

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Section: Classificationmentioning

confidence: 99%

mentioning

confidence: 99%

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

Xiao

et al. 2021

Front. Syst. Neurosci.

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“…A PCA introduced using a distance-based change point detector provided a sensitivity rate of 87% [63]. A fuzzy rule-based and layered directed acyclic graph SVM (LDAG-SVM) was developed accordingly, reaching an accuracy of 98% and a sensitivity of 99% [64]. The convolutional neural network (CNN) approach was developed to interpret seizures and non-seizures, which achieved a sensitivity of 81.4% [65].…”

Section: Introductionmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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“…To predict the onset or preonset of epilepsy in the process of seizure detection, machine learning and pattern classification algorithms are generally applied to classify the EEG signals after extracting the characteristics of the time domain, frequency domain, time frequency domain, or nonlinear domain of the EEG. With the development of computer technology and digital signal processing technology, more and more methods are widely used in the study of seizure detection methods and have achieved certain research results, such as the Bayesian classifier [ 4 ], artificial neural network [ 5 – 9 ], support vector machine (SVM) [ 10 – 13 ], and fuzzy reasoning [ 14 , 15 ]. For example, Obeyli extracted the Lyapunov exponential features of EEG signals and used probabilistic neural networks to classify EEG signals, so as to achieve high classification results [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Chan et al extracted the time-frequency features of five subbands in the wavelet transform domain of epilepsy EEG signals and then used support vector machines and cluster regression models to recognize the onset of seizures. Aarabi et al [ 14 ] extracted the features such as sample entropy, dominant frequency, average amplitude, and amplitude variation coefficient of intracranial EEG da4ta of patients with epilepsy and used the established fuzzy inference rules to fuse the EEG feature information for seizure detection. Although many of the above intelligent classification methods have shown the effectiveness of epilepsy EEG signal classification, they still face a challenge, that is, it is very hard to get enough EEG data for epilepsy to train the model in real life.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

Yao

Cui

2020

Computational and Mathematical Methods in Medicine

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Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients’ health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.

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Epileptic seizure detection using fuzzy-rules-based sub-band specific features and layered multi-class SVM

Cited by 33 publications

References 30 publications

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

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