Matches in SemOpenAlex for { <https://semopenalex.org/work/W3109486590> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W3109486590 abstract "Epilepsy is a common chronic neurological disease of the brain and becomes the second critical neurological disease following the cerebrovascular disease. It has great significance of studying epileptic electroencephalogram (EEG) signals to reduce the frequency of seizure or even prevent epilepsy, thereby decrease the incidence of disability and death induced by epilepsy. Therefore, we proposed an epileptic EEG signals prediction algorithm by using the random forest (RF). The scalp EEG signals used here is weaker than the intracranial EEG signals but easier to collect. The prediction process had four steps: epileptic scalp EEG signals preprocessing by the discrete wavelet transform (DWT), features extraction by several linear measures in the time domain, comparison of the general model and the patient-specific model, and prediction optimization by RF with different features and sub-bands combination. It is found that patient-specific models had higher accuracy, and the coefficient of variation (CV) had a great influence on the seizure prediction. The influence of different sub-bands on the seizure prediction was also investigated. Finally, CV with all sub-bands were optimally selected, and the average accuracy of the seizure prediction was up to 99.8102%. The effectiveness of the proposed prediction algorithm was verified by numerical calculation." @default.
- W3109486590 created "2020-12-07" @default.
- W3109486590 creator A5009967329 @default.
- W3109486590 creator A5013908505 @default.
- W3109486590 creator A5019373318 @default.
- W3109486590 creator A5033229269 @default.
- W3109486590 creator A5064135095 @default.
- W3109486590 creator A5065665700 @default.
- W3109486590 date "2020-10-17" @default.
- W3109486590 modified "2023-09-24" @default.
- W3109486590 title "Epileptic Seizure Prediction from the Scalp EEG Signals by using Random Forest Algorithm" @default.
- W3109486590 cites W1978437325 @default.
- W3109486590 cites W2025744689 @default.
- W3109486590 cites W2030925257 @default.
- W3109486590 cites W2047675462 @default.
- W3109486590 cites W2068372524 @default.
- W3109486590 cites W2105131160 @default.
- W3109486590 cites W2140794498 @default.
- W3109486590 cites W2155632266 @default.
- W3109486590 cites W2261059368 @default.
- W3109486590 cites W2270296195 @default.
- W3109486590 cites W2418678087 @default.
- W3109486590 cites W2460566906 @default.
- W3109486590 cites W2521002893 @default.
- W3109486590 cites W2799610518 @default.
- W3109486590 cites W2975075171 @default.
- W3109486590 cites W2980823296 @default.
- W3109486590 cites W2988395158 @default.
- W3109486590 cites W2996538753 @default.
- W3109486590 cites W2998594188 @default.
- W3109486590 doi "https://doi.org/10.1109/cisp-bmei51763.2020.9263641" @default.
- W3109486590 hasPublicationYear "2020" @default.
- W3109486590 type Work @default.
- W3109486590 sameAs 3109486590 @default.
- W3109486590 citedByCount "2" @default.
- W3109486590 countsByYear W31094865902023 @default.
- W3109486590 crossrefType "proceedings-article" @default.
- W3109486590 hasAuthorship W3109486590A5009967329 @default.
- W3109486590 hasAuthorship W3109486590A5013908505 @default.
- W3109486590 hasAuthorship W3109486590A5019373318 @default.
- W3109486590 hasAuthorship W3109486590A5033229269 @default.
- W3109486590 hasAuthorship W3109486590A5064135095 @default.
- W3109486590 hasAuthorship W3109486590A5065665700 @default.
- W3109486590 hasConcept C105702510 @default.
- W3109486590 hasConcept C11413529 @default.
- W3109486590 hasConcept C153180895 @default.
- W3109486590 hasConcept C154945302 @default.
- W3109486590 hasConcept C15744967 @default.
- W3109486590 hasConcept C169258074 @default.
- W3109486590 hasConcept C169760540 @default.
- W3109486590 hasConcept C2778186239 @default.
- W3109486590 hasConcept C2778515351 @default.
- W3109486590 hasConcept C2779334592 @default.
- W3109486590 hasConcept C28490314 @default.
- W3109486590 hasConcept C34736171 @default.
- W3109486590 hasConcept C41008148 @default.
- W3109486590 hasConcept C522805319 @default.
- W3109486590 hasConcept C71924100 @default.
- W3109486590 hasConceptScore W3109486590C105702510 @default.
- W3109486590 hasConceptScore W3109486590C11413529 @default.
- W3109486590 hasConceptScore W3109486590C153180895 @default.
- W3109486590 hasConceptScore W3109486590C154945302 @default.
- W3109486590 hasConceptScore W3109486590C15744967 @default.
- W3109486590 hasConceptScore W3109486590C169258074 @default.
- W3109486590 hasConceptScore W3109486590C169760540 @default.
- W3109486590 hasConceptScore W3109486590C2778186239 @default.
- W3109486590 hasConceptScore W3109486590C2778515351 @default.
- W3109486590 hasConceptScore W3109486590C2779334592 @default.
- W3109486590 hasConceptScore W3109486590C28490314 @default.
- W3109486590 hasConceptScore W3109486590C34736171 @default.
- W3109486590 hasConceptScore W3109486590C41008148 @default.
- W3109486590 hasConceptScore W3109486590C522805319 @default.
- W3109486590 hasConceptScore W3109486590C71924100 @default.
- W3109486590 hasLocation W31094865901 @default.
- W3109486590 hasOpenAccess W3109486590 @default.
- W3109486590 hasPrimaryLocation W31094865901 @default.
- W3109486590 hasRelatedWork W1894044533 @default.
- W3109486590 hasRelatedWork W2240965754 @default.
- W3109486590 hasRelatedWork W2275058042 @default.
- W3109486590 hasRelatedWork W2391959412 @default.
- W3109486590 hasRelatedWork W2466809621 @default.
- W3109486590 hasRelatedWork W2594111781 @default.
- W3109486590 hasRelatedWork W2800575390 @default.
- W3109486590 hasRelatedWork W3117543832 @default.
- W3109486590 hasRelatedWork W4225299985 @default.
- W3109486590 hasRelatedWork W4313203779 @default.
- W3109486590 isParatext "false" @default.
- W3109486590 isRetracted "false" @default.
- W3109486590 magId "3109486590" @default.
- W3109486590 workType "article" @default.