Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.
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DOI：
10.1371/journal.pone.0096235
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Toumazou C
中科院分区：
文献类型：
--
作者：
Eftekhar A;Juffali W;El-Imad J;Constandinou TG;Toumazou C
这项工作提出了一种将符号动力学方法与 Ngram 算法相结合的新方法，用于检测和预测癫痫发作。所提出的方法在数据预处理后特别应用基于 Ngram 的模式识别，并使用相似性度量（包括汉明距离和 Needlman-Wunsch 算法）来识别时间周期内的独特模式。每个时期内的模式计数用作确定癫痫发作检测和预测标记的措施。使用 21 名患者 623 小时的颅内皮质电图记录（总共 87 次癫痫发作），对该方法的灵敏度和错误预测/检测率进行了量化。使用每个病例中的单独癫痫发作来量化结果，以训练阈值和预测时间窗口。进一步研究了预测能力的统计显着性。我们表明，本文提出的方法对高达 100% 的颞叶病例具有显着的预测能力，灵敏度高达 70-100%，错误预测率低（取决于训练程序）。错误预测最高的案例出现在额叶原点，每小时有 0.31-0.61 次错误预测，21 例中有 18 例具有显着性。平均而言，在最佳情况下，预测灵敏度为 93.81%，错误预测率约为每小时 0.06 个错误预测。与之前使用相同数据集的工作相比，该数据集的灵敏度高达 40-50%，错误预测率低于 0.15/小时。
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.
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