A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation

Peng, Hong; Li, Cancheng; Chao, Jinlong; Wang, Tao; Zhao, Chengjian; Huo, Xiaoning; Hu, Bin

doi:10.1016/j.neucom.2019.12.010

Cited by 46 publications

(12 citation statements)

References 52 publications

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“…There are many works that study patient-independent seizure detection, e.g., [1] employs a real-time method based on dictionary learning and sparse representation; [22] employs a method based on a deep neural network that combines a In order to adapt to the variation of seizure characteristics among patients and at different times, some patient-specific seizure detection algorithms have been proposed. Most of them suppose having enough labeled EEG data and try to improve the patient-specific detector's performance by using elaborate models and features, e.g., [2] uses a group of SVMs and features extracted through empirical mode decomposition (EMD) and common space patterns (CSP); [17] uses a voting SVM system and features containing both the temporal-domain and spectral-domain information of EEG; [23] uses a RVM model and the harmonic multiresolution and self-similarity-based fractal features from EEG data; [16] builds a predictor based on spatio-temporal-spectral hierarchical GCN with an active pre-ictal interval learning scheme (STS-HGCN-AL).…”

Section: Related Work a Patient-specific Seizure Detectionmentioning

confidence: 99%

“…In order to relieve doctors' burden and improve efficiency, many machine-learning-based methods are proposed to build automatic seizure detectors of EEG, such as the dictionarylearning-based method [1], SVM-based method [2], GMMbased method [3], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, the proposed method could quickly train a good Student model for a specific patient at his/her first epilepsy diagnosis with the help of: (1) an experienced Mentor model that chooses the most category-certain EEG data segments;…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Work a Patient-specific Seizure Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…2. SRC is used in many applications [48], especially in face recognition. SGFB is an extended approach of the SRC, which can provide the new idea in MI classification tasks.…”

Section: Sparse Group Filter Bank (Sgfb)mentioning

confidence: 99%

Motor-imagery classification model for brain-computer interface: a sparse group filter bank representation model

Li,

Qin,

Fang

2021

Preprint

Self Cite

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Background: Common spatial pattern (CSP) has been widely used for feature extraction in the case of motor imagery (MI) electroencephalogram (EEG) recordings and in MI classification of brain-computer interface (BCI) applications. BCI usually requires relatively long EEG data for reliable classifier training. More specifically, before using general spatial patterns for feature extraction, a training dictionary from two different classes is used to construct a compound dictionary matrix, and the representation of the test samples in the filter band is estimated as a linear combination of the columns in the dictionary matrix. New method: To alleviate the problem of sparse small sample (SS) between frequency bands. We propose a novel sparse group filter bank model (SGFB) for motor imagery in BCI system. Results: We perform a task by representing residuals based on the categories corresponding to the non-zero correlation coefficients. Besides, we also perform joint sparse optimization with constrained filter bands in three different time windows to extract robust CSP features in a multi-task learning framework. To verify the effectiveness of our model, we conduct an experiment on the public EEG dataset of BCI competition to compare it with other competitive methods.

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“…Inspired by this idea, Tang et al [31] achieved the sparse classification of rolling bearing faults using the reconstruction error-based SRC with compressive sensing strategy. The dictionary learning-based sparse classification approaches were developed for planet bearing fault diagnosis [32] and epilepsy detection [33]. Han et al [34] incorporated K-SVD for dictionary learning with the reconstruction error-based SRC method for wind turbine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

et al. 2021

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Fault diagnosis technique is the core of Prognostics and Health Management (PHM) system, which plays a crucial role in the intelligent operation and maintenance of various rotating machineries. In this paper, we present a novel sparse representation classification framework with structured dictionary design strategy (SRC-SDD) for intelligent fault diagnosis of rotating machineries. The proposed SRC-SDD method consists of two stages, i.e., the structured dictionary design stage and the sparsity-based intelligent diagnosis stage. In the first stage, the novelty of SRC-SDD lies in the overlapping segmentation strategy for structured dictionary design, which leverages the structured prior knowledge of rotating machinery vibration signals, namely, the periodic self-similarity and shift-invariance properties. In the second stage, SRC-SDD achieves fault recognitions of testing samples using a sparsity-based diagnosis strategy based on the minimum sparse reconstruction error. The proposed structured dictionary design strategy can enhance the representation power of dictionaries and thus promote the recognition performance of the sparsity-based diagnosis strategy. Finally, the effectiveness of SRC-SDD has been validated on the gearbox fault dataset from IEEE PHM society. The diagnosis results show that SRC-SDD achieves the excellent recognition accuracy of 100% for predicting six different gearbox health states. Further, the comparative studies with three conventional SRC methods prove the superiority of SRC-SDD in terms of both the recognition performance and computation efficiency.

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A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation

Cited by 46 publications

References 52 publications

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

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

Motor-imagery classification model for brain-computer interface: a sparse group filter bank representation model

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

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