title: Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
abstract: 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.
authors.affiliation: Centre for Bio-Inspired Technology, Part of the Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.
authors.forename: Amir
authors.initials: A
authors.lastname: Eftekhar
authors.affiliation: Centre for Bio-Inspired Technology, Part of the Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.
authors.forename: Walid
authors.initials: W
authors.lastname: Juffali
authors.affiliation: Centre for Bio-Inspired Technology, Part of the Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.
authors.forename: Jamil
authors.initials: J
authors.lastname: El-Imad
authors.affiliation: Centre for Bio-Inspired Technology, Part of the Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Constandinou
authors.affiliation: Centre for Bio-Inspired Technology, Part of the Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.
authors.forename: Christofer
authors.initials: C
authors.lastname: Toumazou
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D005189:False Positive Reactions; D006801:Humans; D046709:Pattern Recognition, Physiological; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014584:User-Computer Interface