Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Rizal, Achmad; Hadiyoso, Sugondo

doi:10.1155/2018/8463256

Cited by 37 publications

(19 citation statements)

References 15 publications

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“…SampEn is one method that is widely used to measure signal complexity. In a research conducted by Rizal [17], it was proven that SampEn can provide high accuracy in the case of epileptic EEG classifications.…”

Section: B Hjorth Descriptormentioning

confidence: 99%

“…This proposed study focuses on time series analysis methods using Hjorth Descriptor and Sample Entropy for ECG biometrics. These methods have been selected for having good performance based upon some previous research to classify ECG and Epileptic EEG signals [15]- [17]. Both of these methods are basically used for analysis of signal complexity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

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In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine. A total of 65 raw ECG waves from the 10 subjects were analyzed. This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features. Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal. The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor. Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.

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Section: B Hjorth Descriptormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, some researches focus on nonlinear techniques to capture the nonlinearity nature of the EEG epilepsy signal. Different types of entropy have been investigated such as approximate entropy [38][39][40], sample entropy [41]. Empirical mode decomposition is used to extract statistical features [42].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Saminu

Zhang

et al. 2020

Elektrika

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These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.

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“…The problem of automatic seizure detection can be divided into two main steps, feature extraction and classifier training. Considerable amounts of works have been done with this two-step procedure for better detection accuracy, including time-frequency feature map with a support vector machine (SVM) [3,7], nonlinear features with different types of classifiers [5,8,9], and features based on time-frequency image with image recognition methods [10,11]. These researches have provided different methodologies for seizure detection.…”

Section: Introductionmentioning

confidence: 99%

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

et al. 2020

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Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

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Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Cited by 37 publications

References 15 publications

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

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