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Journal ArticleDOI

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System

01 Sep 2017-Vol. 25, Iss: 12, pp 2270-2284
TL;DR: Transductive transfer learning is used to reduce the discrepancy in data distribution between the training and testing data, semi-supervised learning is employed to use the unlabeled testing data to remedy the shortage of training data, and TSK fuzzy system is adopted to increase model interpretability.
Abstract: Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.
Citations
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Journal ArticleDOI
TL;DR: Four main methods of transfer learning are described and their practical applications in EEG signal analysis in recent years are explored.

184 citations

Journal ArticleDOI
Meidi Sun1, Hui Wang1, Ping Liu1, Shoudao Huang1, Peng Fan1 
TL;DR: The results for data from the Case Western Reserve University Bearing Data Center show that the proposed SSDAE-TL algorithm is feasible and easy to implement for the fault diagnosis of bearings.

127 citations

Journal ArticleDOI
23 Mar 2020
TL;DR: The state-of-the-art in sleep-monitoring technologies are introduced, the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings are discussed, and the strengths and limitations of current and emerging sensing methods are explored.
Abstract: In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human–computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

113 citations

Journal ArticleDOI
TL;DR: This paper proposes an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation and shows that the proposed fuzzy system has smaller drowsness estimation errors and higher interpretability than introduced benchmarking models.
Abstract: In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.

100 citations


Cites background from "Seizure Classification From EEG Sig..."

  • ...To this end, transfer learning (TL) [5]–[7] can be adopted for driver drowsiness estimation since it is able to take full advantage of knowledge/data from other existing subjects....

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Journal ArticleDOI
11 Sep 2019
TL;DR: Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD.
Abstract: Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram (EEG) signals of patients with epilepsy, their conditions can be monitored and seizure can be detected and intervened in time. As the identification of effective features in EEG signals is important for accurate seizure detection, this paper proposes a multi-view deep feature extraction method in attempt to achieve this goal. The method first uses fast Fourier transform (FFT) and wavelet packet decomposition (WPD) to construct the initial multi-view features. Convolutional neural network (CNN) is then used to automatically learn deep features from the initial multi-view features, which reduces the dimensionality and obtain the features with better seizure identification ability. Furthermore, the multi-view Takagi-Sugeno-Kang fuzzy system (MV-TSK-FS), an interpretable rule-based classifier, is used to construct a classification model with strong generalizability based on the deep multi-view features obtained. Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD. The classification accuracy is also at least 4% higher than the average accuracy achieved with single-view deep features.

97 citations


Cites background from "Seizure Classification From EEG Sig..."

  • ...A variety of epilepsy detection algorithms have been proposed in recent years [8], [10], [12], [14], [22]....

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References
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Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations


"Seizure Classification From EEG Sig..." refers methods in this paper

  • ...Many different methods, including decision tree [9], naïve Bayes [7], [9], support vector machine [6], [11], nearest-mean [7] and linear discriminant analysis [5], [8], [10], have been applied....

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  • ...Many classical machine leaning methods have been studied for this purpose, such as decision tree [9], naïve Bayes [7], [9], support vector machine [6], [11], nearest-mean [7] and linear discriminant analysis [5], [8], [10]....

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Journal ArticleDOI
01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Abstract: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented. The premise of an implication is the description of fuzzy subspace of inputs and its consequence is a linear input-output relation. The method of identification of a system using its input-output data is then shown. Two applications of the method to industrial processes are also discussed: a water cleaning process and a converter in a steel-making process.

18,803 citations


"Seizure Classification From EEG Sig..." refers methods in this paper

  • ...The Mamdani model [20] and the TSK model [21], [22], [38] are two popular fuzzy system models....

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Journal ArticleDOI
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Abstract: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

18,616 citations


"Seizure Classification From EEG Sig..." refers background or methods in this paper

  • ...To cope with this issue, transfer learning (TL) [13]–[18], [40], [42], particularly, large margin projection (LMPROJ) [18], has been used to reduce the data distribution mismatch between the training and testing data....

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  • ...TL [13] is a well-known approach for handling data distribution discrepancy....

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Book
31 Jul 1981
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

15,662 citations


"Seizure Classification From EEG Sig..." refers methods in this paper

  • ...u jk denotes the fuzzy membership and can be obtained by Fuzzy C-means (FCM) clustering or the likes [30], [39]....

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  • ...Specifically, a novel FCM-like [30] SSL approach is designed for label clustering:...

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  • ...Specifically, a novel FCM-like [30] SSL approach is designed for label clustering: min JSS L(U) = C∑ j=1 NT∑ i=1 μmi j ∥∥∥pTg,Sxgi,T − θ j ∥∥∥ 2 s.t . μi j ∈ [0, 1] and C∑ j=1 μi j =1 (9) Authorized licensed use limited to: Huazhong University of Science and Technology....

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Journal Article
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Abstract: While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.

10,306 citations


"Seizure Classification From EEG Sig..." refers methods in this paper

  • ...To evaluate whether the performance difference among the algorithms were statistically significant, Friedman test [36], [37] and the Holm post hoc test [36], [37] were performed....

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