Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

Wen, Tingxi; Zhang, Zhongnan

doi:10.1097/md.0000000000006879

Cited by 59 publications

(31 citation statements)

References 42 publications

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“…These methods have been applied to both interictal and ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity (Wendung et al, 2009). Wen et al proposed a genetic algorithm-based frequency domain feature search method that exhibited good extensibility (Wen and Zhang, 2017). Therefore, we conducted this study based on frequency domain signals and compared the seizure detection performances of both the frequency and time domains.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Based on EEG Signals and CNN

Zhou

Tian

Cao

et al. 2018

Front. Neuroinform.

357

163

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Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.

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Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Based on EEG Signals and CNN

Zhou

Tian

Cao

et al. 2018

Front. Neuroinform.

357

163

View full text Add to dashboard Cite

show abstract

“…Feature extraction is a necessary stage before classification for EEG signal recognition. In general, time domain and frequency domain feature extractions are two types of feature extraction methods (Wen and Zhang, 2017 ). In our experiments, we extract EEG features using kernel principal component analysis (KPCA) and short-time Fourier transform (STFT) (Blanco et al, 1997 ).…”

Section: Methodsmentioning

confidence: 99%

An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive Learning

Zhang

2020

Front. Neurosci.

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Epilepsy is an abnormal function disease of movement, consciousness, and nerve caused by abnormal discharge of brain neurons in the brain. EEG is currently a very important tool in the process of epilepsy research. In this paper, a novel noise-insensitive Takagi–Sugeno–Kang (TSK) fuzzy system based on interclass competitive learning is proposed for EEG signal recognition. First, a possibilistic clustering in Bayesian framework with interclass competitive learning called PCB-ICL is presented to determine antecedent parameters of fuzzy rules. Inherited by the possibilistic c -means clustering, PCB-ICL is noise insensitive. PCB-ICL learns cluster centers of different classes in a competitive relationship. The obtained clustering centers are attracted by the samples of the same class and also excluded by the samples of other classes and pushed away from the heterogeneous data. PCB-ICL uses the Metropolis–Hastings method to obtain the optimal clustering results in an alternating iterative strategy. Thus, the learned antecedent parameters have high interpretability. To further promote the noise insensitivity of rules, the asymmetric expectile term and Ho–Kashyap procedure are adopted to learn the consequent parameters of rules. Based on the above ideas, a TSK fuzzy system is proposed and is called PCB-ICL-TSK. Comprehensive experiments on real-world EEG data reveal that the proposed fuzzy system achieves the robust and effective performance for EEG signal recognition.

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“…Consequently, application of multiple processes may often affect feature redundancy and expansion of feature dimension. Feature selection reduces the dimension of feature space and minimizes the data training and application [26]. In this study, we conducted a simple feature selection based on the importance of each feature evaluated by random forest algorithm.…”

Section: Feature Selection and Optimizationmentioning

confidence: 99%

Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

et al. 2020

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Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

Cited by 59 publications

References 42 publications

Epileptic Seizure Detection Based on EEG Signals and CNN

Epileptic Seizure Detection Based on EEG Signals and CNN

An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive Learning

Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

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