dc.creatorGarces Correa, Maria Agustina
dc.creatorOrosco, Lorena Liliana
dc.creatorDiez, Pablo Federico
dc.creatorLaciar Leber, Eric
dc.date.accessioned2021-11-30T05:40:10Z
dc.date.accessioned2022-10-15T01:11:03Z
dc.date.available2021-11-30T05:40:10Z
dc.date.available2022-10-15T01:11:03Z
dc.date.created2021-11-30T05:40:10Z
dc.date.issued2019-12
dc.identifierGarces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Adaptive Filtering for Epileptic Event Detection in the EEG; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 39; 6; 12-2019; 912-918
dc.identifier1609-0985
dc.identifierhttp://hdl.handle.net/11336/147667
dc.identifier2199-4757
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4328573
dc.description.abstractPurpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve signifcantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive flters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as ofine. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive flters.
dc.languageeng
dc.publisherInstitute of Biomedical Engineering
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs40846-019-00467-w
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/s40846-019-00467-w
dc.rightshttps://creativecommons.org/licenses/by/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectADAPTIVE FILTER
dc.subjectEEG
dc.subjectEPILEPTIC SEIZURE
dc.subjectSIGNAL PROCESSING
dc.titleAdaptive Filtering for Epileptic Event Detection in the EEG
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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