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

2017 ◽  
Vol 25 (12) ◽  
pp. 2270-2284 ◽  
Author(s):  
Yizhang Jiang ◽  
Dongrui Wu ◽  
Zhaohong Deng ◽  
Pengjiang Qian ◽  
Jun Wang ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Transfer Learning ◽  
Fuzzy System ◽  
Eeg Signals ◽  
Seizure Classification

scholarly journals Identification of Epileptic EEG Signals Through TSK Transfer Learning Fuzzy System

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhaoliang Zheng ◽  
Xuan Dong ◽  
Jian Yao ◽  
Leyuan Zhou ◽  
Yang Ding ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Fuzzy System ◽  
Marginal Distribution ◽  
Data Distribution ◽  
Eeg Signals ◽  
Training Set ◽  
Proposed Model ◽  
Class Information ◽  
Intelligent Recognition ◽  
Distribution Distance

We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require that the training set and test set have the same distribution when recognizing EEG signals, some only consider reducing the marginal distribution distance of the data while ignoring the intra-class information of data, and some lack of interpretability. To address these deficiencies, we construct a TSK transfer learning fuzzy system (TSK-TL) based on the easy-to-interpret TSK fuzzy system the transfer learning method. The proposed model is interpretable. By using the information contained in the source domain and target domains more effectively, the requirements for data distribution are further relaxed. It realizes the identification of epilepsy EEG signals in data drift scene. The experimental results show that compared with the existing algorithms, TSK-TL has better performance in EEG recognition of epilepsy.

Classification of EEG signals in epilepsy using a novel integrated TSK fuzzy system

2020 ◽  
pp. 1-16
Author(s):  
Yuwen Tao ◽  
Yizhang Jiang ◽  
Kaijian Xia ◽  
Jing Xue ◽  
Leyuan Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Fuzzy System ◽  
Domain Adaptation ◽  
Explanatory Power ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Training Samples

The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods.

scholarly journals Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1807
Author(s):  
Sascha Grollmisch ◽  
Estefanía Cano
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
State Of The Art ◽  
Training Data ◽  
Audio Classification ◽  
Image Domain ◽  
Full Dataset ◽  
Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

scholarly journals An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yufeng Yao ◽  
Zhiming Cui
Keyword(s):  
Transfer Learning ◽  
Real Life ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Least Squares Regression ◽  
Eeg Signals ◽  
Strong Basis ◽  
Inductive Transfer ◽  
Inductive Transfer Learning ◽  
The Impact

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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