Multifuse multilayer multikernel RVFLN+ of process modes decomposition and approximate entropy data from iEEG/sEEG signals for epileptic seizure recognition

Rout, Susanta Kumar; Dash, P.K.; Biswal, Pradyut Kumar

doi:10.1016/j.compbiomed.2021.104299

Cited by 19 publications

(12 citation statements)

References 56 publications

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“…With the development of science and technology, LMD has been widely used in many fields. LMD can transform a complex original signal into a product function component with instantaneous significance through a series of operations, and the resulting component frequencies are arranged from high to low [19].…”

Section: Feature Extraction Of Approximate Entropy and Lmdmentioning

confidence: 99%

A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching

Cai

McDonald²

2023

Int. J. Emerg. Technol. Learn.

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With the rapid development of science and technology, intelligent teaching has been gradually popularized in the campus, and the change of relevant teaching modes has also been carried out. In the new English teaching, it is necessary to analyze the students’ psychological behavior. In order to get accurate results, the research captures the students’ behavior as a behavior sequence, and uses the improved combined kernel support vector machine to extract features on the basis of approximate entropy and local mean decomposition. At the same time, it is compared with the traditional support vector machine, the traditional least square vector machine and the back-propagation neural network algorithm. The experimental results show that the improved algorithm has higher accuracy, recall and accuracy than the other three algorithms. The overall accuracy of the improved algorithm is 82.53%, the highest accuracy is 90.12%, the overall recall is 58.64% and the overall precision is 86.92%. The experimental results show that the improved algorithm has a significant improvement in performance, and can be applied to the extraction of students’ psychological characteristics. Therefore, the algorithm can be used in conventional blended English teaching, which is convenient for teachers to carry out English teaching more efficiently.

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Section: Feature Extraction Of Approximate Entropy and Lmdmentioning

confidence: 99%

A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching

Cai

McDonald²

2023

Int. J. Emerg. Technol. Learn.

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“…However, since the number K of sample eigenvectors is generally much larger than the number L of hidden layer nodes, an appropriate solution may not be found in equation (15). Therefore, the output weight β from hidden layer to output layer is solved by the least square method: † 1 ( / )…”

Section: Fuzzy Entropymentioning

confidence: 99%

“…H is defined as Moore Penrose generalized inverse. Using orthogonal projection, a small positive value / I  is added to the diagonal to prevent β from generating singularity [15] .…”

Section: Fuzzy Entropymentioning

confidence: 99%

Low voltage series arc fault identification method based on VMD and RVFL neural network

Tian

Wang³

et al. 2022

J. Phys.: Conf. Ser.

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In low-voltage distribution system, series arc fault is one of the main causes of fire accidents. The series arc current characteristics of load are similar to its normal working current, and the ordinary current protection device cannot detect it effectively. In this paper, an arc recognition method based on variational modal decomposition (VMD) and random vector functional link (RVFL) neural network is proposed. The series arc current data of different loads are collected by the low-voltage series arc experimental platform, and the arc current characteristics are analyzed. Four different intrinsic mode functions (IMF) are obtained by variational modal decomposition, and their fuzzy entropy is calculated respectively to form an eigenvector data set, which is input into RVFL neural network for training and identification. In this paper, approximate entropy (ApEn), sample entropy (SampEn) and fuzzy entropy (FuzzyEn) are extracted as feature vectors for each IMF of VMD, their discrimination for different loads is analyzed, and the recognition accuracy of RVFL neural network compared with SVM and BP neural network algorithm is compared. It is verified that the arc recognition method proposed in this paper has better recognition effect.

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“…The independent datasets consist of patients from different age groups (neonatal, paediatric, and adult) and types (humans and dogs). In comparison, existing seizure detectors for sEEGs and iEEGs are often trained and analyzed separately, and are usually trained and tested on the same dataset 30,42 (with a few exceptions, see [43][44][45] for example).…”

Section: Introductionmentioning

confidence: 99%

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

Peh

Thangavel

Yao

et al. 2022

Preprint

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Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last several hours or even days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse morphologies and characteristics across different patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG (sEEG) and intracranial EEG (iEEG). First, we deploy a convolutional neural network (CNN) with transformers (TRF) and belief matching (BM) loss to detect seizures in single-channel EEG segments (channel-level detection). Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments (segment-level detection). At last, we apply postprocessing filters to the segment-level outputs to determine the start and end points of seizures in multi-channel EEGs (EEG-level detection). We introduce the minimum overlap evaluation scoring (MOES) as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) sEEG dataset and evaluated it on five other independent sEEG and iEEG datasets. On the TUH-SZ dataset, the proposed patient-independent seizure detector achieves a sensitivity (SEN), precision (PRE), average and median false positive rate per hour (aFPR/h and mFPR/h), and median offset of 0.772, 0.429, 4.425, 0, and -2.125s, respectively. Across all four adult datasets (excluding neonatal and paediatric datasets), we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. Meanwhile, on neonatal and paediatric datasets, we obtained SEN of 0.227-0.678, PRE of 0.377-0.818, aFPR/h of 0.253-0.421, and mFPR/h of 0.118-0.223. The proposed seizure detector can reliably detect seizures in adult EEGs (to less extent in neonatal EEGs) and takes less than 15s for a 30 minutes EEG. Hence, this system could potentially aid the clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.

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Multifuse multilayer multikernel RVFLN+ of process modes decomposition and approximate entropy data from iEEG/sEEG signals for epileptic seizure recognition

Cited by 19 publications

References 56 publications

A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching

A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching

Low voltage series arc fault identification method based on VMD and RVFL neural network

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

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