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.
Highlights
Epilepsy is a neurological disorder that affects approximately 1% of the world’s population
A multitude of fields have studied the underlying mechanisms behind seizures, looking at the brain from multiple perspectives including bottom-up to global approaches
As an example, during a seizure onset, the number of unique large patterns reduces, while the smaller pattern sizes increase somewhat. We found at this stage of the algorithm that the increase in smaller patterns is partially due to variations in patterns symbols that are subsets of the larger pattern lengths; a member of the smaller pattern size varies in symbol classification and it is missed as being a subset of the larger pattern
Summary
Epilepsy is a neurological disorder that affects approximately 1% of the world’s population. It is characterised by seizures, which can manifest in several ways, from simple loss of awareness to more severe motor movements with loss of consciousness. The goal of this work is in the detection and prediction of the epileptic seizure. 70% of people with epilepsy can have it controlled with the correct anti-epileptic drugs. For those not helped by medication there are options, including surgery, which is successful in up to 70% of cases For those not helped by medication there are options, including surgery, which is successful in up to 70% of cases (http://www.epilepsynse. org.uk/)
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