An Optimal Control Approach to Seizure Detection in Drug-Resistant Epilepsy
An Optimal Control Approach to Seizure Detection in Drug-Resistant Epilepsy
复制标题
耐药性癫痫发作检测的最佳控制方法
DOI：
10.1007/978-94-017-9041-3_6
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
S. Sarma
中科院分区：
文献类型：
--
作者：
S. Santaniello;S. Burns;W. Anderson;S. Sarma
在大脑等复杂的生物系统中，隐藏状态转换是频繁发生的事件。从顺序测量(例如，EEG、MER、EMG等)中准确检测这些转变。在工程学和医学之间的几个应用中起着关键作用，如神经假体、脑机接口和药物输送，但到目前为止开发的检测方法普遍缺乏稳健性。我们最近通过开发一种结合了最优控制和马尔可夫过程的贝叶斯检测范式来解决这个问题。神经活动被描述为由隐马尔可夫模型(HMM)产生的随机过程，检测策略使误报概率和准确率(即估计的转换时间和实际转换时间之间的滞后)的损失函数最小化。该策略产生一个时变阈值，该阈值应用于状态转移的后验贝叶斯概率，并基于HMM的演变以及误报和准确性的相对损失，自动适应每个新获取的测量。本文报道了所提出的范例在耐药癫痫受试者癫痫发作自动在线检测中的应用。
Hidden state transitions are frequent events in complex biological systems like the brain. Accurately detecting these transitions from sequential measurements (e.g., EEG, MER, EMG, etc.) is pivotal in several applications at the interface between engineering and medicine, like neural prosthetics, brain-computer interface, and drug delivery, but the detection methodologies developed thus far generally suffer from a lack of robustness. We recently addressed this problem by developing a Bayesian detection paradigm that combines optimal control and Markov processes. The neural activity is described as a stochastic process generated by a Hidden Markov Model (HMM) and the detection policy minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). The policy results in a time-varying threshold that applies to the a posteriori Bayesian probability of state transition and automatically adapts to each newly acquired measurement, based on the evolution of the HMM and the relative loss for false positives and accuracy. An application of the proposed paradigm to the automatic online detection of seizures in drug-resistant epilepsy subjects is here reported.
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