Baldassano Steven, Wulsin Drausin, Ung Hoameng, Blevins Tyler, Brown Mesha-Gay, Fox Emily, Litt Brian
Department of Bioengineering, University of Pennsylvania, USA. Center for Neuroengineering and Therapeutics, University of Pennsylvania, USA.
J Neural Eng. 2016 Jun;13(3):036011. doi: 10.1088/1741-2560/13/3/036011. Epub 2016 Apr 21.
Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model.
We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control.
Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h(-1)). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)).
This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.
最近，美国食品药品监督管理局（FDA）批准了首款用于治疗癫痫的响应式闭环颅内装置。由于这些装置必须在癫痫发作开始后的数秒内做出响应且不能漏检事件，因此它们被调校为具有高灵敏度，这导致频繁出现误报刺激并缩短了电池寿命。在这项研究中，我们提出了一种更强大的癫痫发作检测模型。
我们使用贝叶斯非参数马尔可夫切换过程将颅内脑电图（iEEG）数据解析为不同的动态事件状态。然后将每个事件状态建模为多维高斯分布，以便进行预测状态分配。通过检测对癫痫发作起始区域具有高度特异性的事件状态，该方法可以识别与癫痫发作活动转变相关的iEEG数据的精确区域，减少与发作间期爆发相关的误报检测。癫痫发作检测算法被转化为实时应用，并在一项小型试点研究中进行了验证，该研究使用了来自两只患有自然发生的多灶性癫痫的犬的391天连续iEEG数据。开发了一种以NeuroPace RNS系统为模型的基于特征的癫痫发作检测器作为对照。
我们新颖的癫痫发作检测方法在漏报率方面有改善（漏报癫痫发作从2/55次降至0/55次），误报率也显著降低（从0.058 h⁻¹降至0.0012 h）。所有癫痫发作在明确的癫痫活动开始前平均12.1 ± 6.9秒被检测到（明确的癫痫发作起始（UEO））。
该算法代表了一种计算成本低、个性化的实时检测方法，适用于植入式抗癫痫装置，相对于当前行业标准，可能会大幅降低误报率。
