FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3177432079> ?p ?o ?g. }

• W3177432079 endingPage "222" @default.
• W3177432079 startingPage "212" @default.
• W3177432079 abstract "Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection." @default.
• W3177432079 created "2021-07-05" @default.
• W3177432079 creator A5014199804 @default.
• W3177432079 creator A5039797614 @default.
• W3177432079 creator A5042441651 @default.
• W3177432079 creator A5042937962 @default.
• W3177432079 creator A5056914205 @default.
• W3177432079 creator A5085903306 @default.
• W3177432079 date "2021-10-01" @default.
• W3177432079 modified "2023-10-12" @default.
• W3177432079 title "One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG" @default.
• W3177432079 cites W2023335485 @default.
• W3177432079 cites W2041875327 @default.
• W3177432079 cites W2053744708 @default.
• W3177432079 cites W2110923261 @default.
• W3177432079 cites W2549100242 @default.
• W3177432079 cites W2568407436 @default.
• W3177432079 cites W2588128256 @default.
• W3177432079 cites W2608948620 @default.
• W3177432079 cites W2613502521 @default.
• W3177432079 cites W2742472784 @default.
• W3177432079 cites W2750384459 @default.
• W3177432079 cites W2759483166 @default.
• W3177432079 cites W2801752756 @default.
• W3177432079 cites W2888598199 @default.
• W3177432079 cites W2894170005 @default.
• W3177432079 cites W2901730235 @default.
• W3177432079 cites W2915149867 @default.
• W3177432079 cites W2916318597 @default.
• W3177432079 cites W2934856817 @default.
• W3177432079 cites W2941297993 @default.
• W3177432079 cites W2946520801 @default.
• W3177432079 cites W2962222774 @default.
• W3177432079 cites W2964267916 @default.
• W3177432079 cites W2964680775 @default.
• W3177432079 cites W2973010960 @default.
• W3177432079 cites W3042886859 @default.
• W3177432079 cites W3045153122 @default.
• W3177432079 cites W3090702473 @default.
• W3177432079 cites W3092702366 @default.
• W3177432079 cites W3095844389 @default.
• W3177432079 cites W3105392613 @default.
• W3177432079 cites W3123303190 @default.
• W3177432079 cites W3126745694 @default.
• W3177432079 cites W3127847667 @default.
• W3177432079 doi "https://doi.org/10.1016/j.neucom.2021.06.048" @default.
• W3177432079 hasPublicationYear "2021" @default.
• W3177432079 type Work @default.
• W3177432079 sameAs 3177432079 @default.
• W3177432079 citedByCount "36" @default.
• W3177432079 countsByYear W31774320792021 @default.
• W3177432079 countsByYear W31774320792022 @default.
• W3177432079 countsByYear W31774320792023 @default.
• W3177432079 crossrefType "journal-article" @default.
• W3177432079 hasAuthorship W3177432079A5014199804 @default.
• W3177432079 hasAuthorship W3177432079A5039797614 @default.
• W3177432079 hasAuthorship W3177432079A5042441651 @default.
• W3177432079 hasAuthorship W3177432079A5042937962 @default.
• W3177432079 hasAuthorship W3177432079A5056914205 @default.
• W3177432079 hasAuthorship W3177432079A5085903306 @default.
• W3177432079 hasBestOaLocation W31774320791 @default.
• W3177432079 hasConcept C105702510 @default.
• W3177432079 hasConcept C127413603 @default.
• W3177432079 hasConcept C153180895 @default.
• W3177432079 hasConcept C154945302 @default.
• W3177432079 hasConcept C15744967 @default.
• W3177432079 hasConcept C169760540 @default.
• W3177432079 hasConcept C17755696 @default.
• W3177432079 hasConcept C21200559 @default.
• W3177432079 hasConcept C24326235 @default.
• W3177432079 hasConcept C2778186239 @default.
• W3177432079 hasConcept C2778515351 @default.
• W3177432079 hasConcept C2779334592 @default.
• W3177432079 hasConcept C41008148 @default.
• W3177432079 hasConcept C522805319 @default.
• W3177432079 hasConcept C71924100 @default.
• W3177432079 hasConcept C76155785 @default.
• W3177432079 hasConcept C81363708 @default.
• W3177432079 hasConcept C82876162 @default.
• W3177432079 hasConcept C98254291 @default.
• W3177432079 hasConceptScore W3177432079C105702510 @default.
• W3177432079 hasConceptScore W3177432079C127413603 @default.
• W3177432079 hasConceptScore W3177432079C153180895 @default.
• W3177432079 hasConceptScore W3177432079C154945302 @default.
• W3177432079 hasConceptScore W3177432079C15744967 @default.
• W3177432079 hasConceptScore W3177432079C169760540 @default.
• W3177432079 hasConceptScore W3177432079C17755696 @default.
• W3177432079 hasConceptScore W3177432079C21200559 @default.
• W3177432079 hasConceptScore W3177432079C24326235 @default.
• W3177432079 hasConceptScore W3177432079C2778186239 @default.
• W3177432079 hasConceptScore W3177432079C2778515351 @default.
• W3177432079 hasConceptScore W3177432079C2779334592 @default.
• W3177432079 hasConceptScore W3177432079C41008148 @default.
• W3177432079 hasConceptScore W3177432079C522805319 @default.
• W3177432079 hasConceptScore W3177432079C71924100 @default.
• W3177432079 hasConceptScore W3177432079C76155785 @default.
• W3177432079 hasConceptScore W3177432079C81363708 @default.
• W3177432079 hasConceptScore W3177432079C82876162 @default.

• next