I-Corps: Epileptic Seizure Detection System

这个i-Corps项目的更广泛的影响/商业潜力将使癫痫患者受益，因为它改进了医生的诊断系统，使他们从手动扫描连续的脑电结果中解脱出来。预计这将大大降低医疗保健成本，并使癫痫患者监测的新商业应用成为可能。该项目中应用的非线性分析技术有可能在神经科学中开辟新的应用，并可能被证明是识别癫痫以及其他神经系统疾病发生的方式和原因的关键。该方法的高分辨率也表明了在识别癫痫前体方面的潜力，这将更好地评估癫痫的易感性，并最终通过外部干预抑制癫痫。这项i-Corps项目进一步开发了一个可靠的自动化癫痫检测机制，这将潜在地帮助医生和癫痫患者。疑似癫痫患者的既定做法是一个人工过程，劳动强度很高，而且容易出现人为错误。这里开发的技术通过利用混沌系统和非线性测量的技术来解决这些问题，与标准的线性测量相比，混沌系统和非线性测量更准确地描述了大脑活动，并在使用脑电数据进行癫痫检测时产生了明显更准确的结果。它结合了来自算法和计算硬件的最先进的解决方案。该方法的核心是最近开发的一种估计Lyapunov指数的算法，该算法被证明提供了无与伦比的分辨率和抗噪性。这种方法的计算代价很高。为了满足应用程序的实时限制，该项目采用了多核CPU/GPU等可并行化平台。此外，还将通过使用神经网络和小波匹配来测试一些形状检测算法，从而实现完全自动化。该方法已经在动物模型和一些人类数据的大量记录上进行了测试。

The broader impact/commercial potential of this I-Corps project will benefit epileptic patients by improving diagnostic systems for doctors, freeing them from manual scanning of continuous electroencephalogram results. This is expected to bring about significant reduction of cost in health care and enable novel commercial applications of monitoring of epileptic patients. The nonlinear analysis technique applied in this project has the potential to open up new applications in neuroscience and may prove to be essential in recognizing how and why seizures occur as well as other neurological disorders. The high resolution of the approach also indicates a potential in identifying seizure precursors, which would better assess seizure susceptibility and ultimately lead to seizure suppression through external intervention.This I-Corps project further develops a reliable and automated seizure detection mechanism which will potentially aid medical practitioners and patients with epilepsy. The established practice for patients suspected of epilepsy is a manual process, highly labor-intensive, and prone to human error. The technology developed here addresses these problems by utilizing techniques from chaotic systems and nonlinear measures that more accurately describe brain activity and yield significantly more accurate results in seizure detection using EEG data compared to standard linear measures. It combines state-of-the-art solutions from both algorithms and computing hardware. At the heart of the method, there is a recently developed algorithm for the estimation of Lyapunov exponents, a measure of chaoticity, that was shown to provide unparalleled resolution and noise immunity. This method is computationally expensive. To adhere to the real-time constraints of the application, the project employs parallelizable platforms such as multi-core CPUs/GPUs. Furthermore, complete automation will be achieved by testing out a number of shape detection algorithms through the use of neural networks and wavelet matching. The method has been tested on extensive records from animal models and some human data.