SOFTWARE TOOL FOR SEIZURE ANALYSIS AND PREDICTION

描述（由申请人提供）：目前缺乏强大的、现成的计算工具来分析不规则间隔的数据，如癫痫发作和治疗干预的效果，以及通过定量分析对治疗进行智能优化。提出了一种用于分析抗癫痫治疗（脑电刺激或BES）效果的统计方法的改进和验证。该应用程序结合了一个统计模型，可以在临床研究期间使用经过验证的癫痫检测算法的自动输出来引出趋势，生物节律对癫痫易感性的影响以及治疗参数变化对癫痫活动的影响，从而导致改进的治疗方法。该方法将在临床BES试验数据库上进行测试，以作为分析和优化治疗的软件工具。将开发一种结合趋势提取和经验模型的经验癫痫发作预测算法（eSPA），使用类似的框架来测试受试者癫痫发作的可预测性。eSPA将在一个广泛的长时间序列人类数据库上进行评估。所提出的方法将从特定数据集确定受试者癫痫发作的可预测性，以及eSPA是否可以在便携式/植入式警告设备中实现。

DESCRIPTION (provided by applicant): There is a dearth of powerful, readily available computational tools for analyzing irregularly spaced data such as seizures and the effect of therapeutic interventions, and for intelligent optimization of therapies through quantitative analysis. The refinement and validation of a statistical methodology for analyzing the effects of an anti-seizure therapy (brain electrical stimulation or BES) are proposed. This application incorporates a statistical model that can elicit trends, influences of biological rhythms on seizure susceptibility and the effects of changes in treatment parameters on epileptic activity using the automated output of a proven seizure detection algorithm over the duration of a clinical study, thus leading to improved therapies. The method will be tested on a database of clinical BES trials for possible implementation as a software tool for analysis and optimization of therapy. An empirical seizure prediction algorithm (eSPA) incorporating trend extraction with empirical models will be developed using a similar framework to test the predictability of a subject's seizures. The eSPA will be evaluated on an extensive human database of long time series. The proposed methods will determine the predictability of a subject's seizures from a certain data set, and whether the eSPA can be implemented in a portable/implantable warning device.