I-Corps: Epileptic Seizure Detection System

这个I-Corps项目的更广泛的影响/商业潜力将通过改善医生的诊断系统使癫痫患者受益，使他们免于手动扫描连续的脑电图结果。这有望显著降低医疗保健成本，并实现癫痫患者监测的新商业应用。 在这个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，并可能被证明是必不可少的认识如何和为什么癫痫发作以及其他神经系统疾病发生。该方法的高分辨率也表明了识别癫痫发作前兆的潜力，这将更好地评估癫痫发作的易感性，并最终导致通过外部干预抑制癫痫发作。对疑似癫痫患者的既定做法是一个手工过程，高度劳动密集型，容易出现人为错误。这里开发的技术通过利用来自混沌系统和非线性测量的技术来解决这些问题，这些技术更准确地描述大脑活动，并且与标准线性测量相比，使用EEG数据在癫痫发作检测中产生更准确的结果。它结合了来自算法和计算硬件的最先进的解决方案。在该方法的核心，有一个最近开发的算法估计的李雅普诺夫指数，衡量混乱，这被证明提供无与伦比的分辨率和抗噪性。 这种方法在计算上是昂贵的。为了满足应用程序的实时约束，该项目采用了可并行化的平台，如多核CPU/GPU。此外，通过使用神经网络和小波匹配测试出一些形状检测算法，将实现完全自动化。该方法已在动物模型和一些人类数据的广泛记录上进行了测试。

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.