Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the H...

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Autores principales:	Amir Eftekhar, Walid Juffali, Jamil El-Imad, Timothy G Constandinou, Christofer Toumazou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f2b24c3c3144f61832d8987825a110f
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spelling	oai:doaj.org-article:7f2b24c3c3144f61832d8987825a110f2021-11-18T08:17:43ZNgram-derived pattern recognition for the detection and prediction of epileptic seizures.1932-620310.1371/journal.pone.0096235https://doaj.org/article/7f2b24c3c3144f61832d8987825a110f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24886714/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.Amir EftekharWalid JuffaliJamil El-ImadTimothy G ConstandinouChristofer ToumazouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e96235 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amir Eftekhar Walid Juffali Jamil El-Imad Timothy G Constandinou Christofer Toumazou Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
description	This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.
format	article
author	Amir Eftekhar Walid Juffali Jamil El-Imad Timothy G Constandinou Christofer Toumazou
author_facet	Amir Eftekhar Walid Juffali Jamil El-Imad Timothy G Constandinou Christofer Toumazou
author_sort	Amir Eftekhar
title	Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
title_short	Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
title_full	Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
title_fullStr	Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
title_full_unstemmed	Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
title_sort	ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7f2b24c3c3144f61832d8987825a110f
work_keys_str_mv	AT amireftekhar ngramderivedpatternrecognitionforthedetectionandpredictionofepilepticseizures AT walidjuffali ngramderivedpatternrecognitionforthedetectionandpredictionofepilepticseizures AT jamilelimad ngramderivedpatternrecognitionforthedetectionandpredictionofepilepticseizures AT timothygconstandinou ngramderivedpatternrecognitionforthedetectionandpredictionofepilepticseizures AT christofertoumazou ngramderivedpatternrecognitionforthedetectionandpredictionofepilepticseizures
_version_	1718421934059814912

Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

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