Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection

Chen, Xuhui; Ji, Jinlong; Ji, Tianxi; Li, Ming

doi:10.1145/3233547.3233566

Cited by 38 publications

(20 citation statements)

References 31 publications

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“…In this section, Golmohammadi et al [ 68 ] evaluated two LSTM architectures with three and four layers together with the Softmax classifier in their investigation and obtained satisfactory results. In [ 92 ], three-layer LSTMs are used for feature extraction and classification. The sigmoid active function is used in the last fully connected (FC) layer for classification.…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

293

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

“…These two gates decide which information is necessary to pass to the output. In one experiment, Chen et al [ 92 ] used a three-layer GRU network with sigmoid classifier and yielded 96.67% accuracy. Talathi et al have used a new CADS based on GRU for epileptic seizure detection [ 103 ].…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

293

109

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“…However, the success of deep learning approaches highly relies on the abundance of labeled training data. In practice, labeling EEG signals does require the expertise of an experienced pathologist and is incredibly time-consuming [ 20 ]. On the contrary, unlabeled data are easy to obtain but hard to utilize for training.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised Seizure Prediction in Scalp EEG Using Consistency Regularization

Deng

Liu

et al. 2022

Journal of Healthcare Engineering

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Early prediction of epilepsy seizures can warn the patients to take precautions and improve their lives significantly. In recent years, deep learning has become increasingly predominant in seizure prediction. However, existing deep learning-based approaches in this field require a great deal of labeled data to guarantee performance. At the same time, labeling EEG signals does require the expertise of an experienced pathologist and is incredibly time-consuming. To address this issue, we propose a novel Consistency-based Semisupervised Seizure Prediction Model (CSSPM), where only a fraction of training data is labeled. Our method is based on the principle of consistency regularization, which underlines that a robust model should maintain consistent results for the same input under extra perturbations. Specifically, by using stochastic augmentation and dropout, we consider the entire neural network as a stochastic model and apply a consistency constraint to penalize the difference between the current prediction and previous predictions. In this way, unlabeled data could be fully utilized to improve the decision boundary and enhance prediction performance. Compared with existing studies requiring all training data to be labeled, the proposed method only needs a small portion of data to be labeled while still achieving satisfactory results. Our method provides a promising solution to alleviate the labeling cost for real-world applications.

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“…VOLUME 4, 2016 The privacy protection of patients' data and the high labeling costs hinder obtaining enough training data. In order to reduce the need for labeled data and improve the quality of labeled data, deep transfer learning [10] and deep active learning (AL) [11] methods have been employed in patientindependent seizure detection [12]- [15] and shown good performance.…”

Section: Introductionmentioning

confidence: 99%

MS4PS: A Mentor-Student Architecture for Patient-Specific Seizure Detection With Combination of Transfer Learning and Active Learning

Liu

Zhu

et al. 2022

IEEE Access

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Privacy protection, high labeling cost, and the varying characteristics of seizures among patients and at different times are the main obstacles to building seizure detection models. In light of these, we propose a novel Mentor-Student architecture for Patient-Specific seizure detection (MS4PS). It contains a new way of knowledge transferring named mentor-select-for-student, which exploits the knowledge of a Mentor model by using this model to select data for training a Student model, making it possible to avoid transferring patients' data and the negative influence of transferring parameters/structures of pretrained models. It also contains a new way of active learning, which uses both an experienced Mentor model and a quick-learning Student model to select high-quality samples for doctors to label. Each of the two models is coupled with a particular sample selection strategy that combines the uncertainty/certainty and the distance between unlabeled samples and labeled seizure samples. The proposed method could quickly train a suitable detector for a patient at his/her first epilepsy diagnosis with the help of: (1) an experienced Mentor model that chooses the most category-certain electroencephalography (EEG) data segments; (2) a Student model (detector itself) that chooses the most category-uncertain EEG data segments; (3) doctors who label these data segments selected by both the Mentor model and Student model. By replacing or improving the Mentor model and refining the historical models of patients when they come next time, the MS4PS system could be sustainedly promoted. The proposed method is tested on CHB-MIT and NEO datasets and the results demonstrate its effectiveness and efficiency.

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Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection

Cited by 38 publications

References 31 publications

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Semisupervised Seizure Prediction in Scalp EEG Using Consistency Regularization

MS4PS: A Mentor-Student Architecture for Patient-Specific Seizure Detection With Combination of Transfer Learning and Active Learning

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