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Detecting Epileptic Seizures Using Deep Learning with Cloud and Fog Computing

机译:通过将深度学习与云和雾计算结合使用来检测癫痫发作

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Chronic diseases are growing exponentially. Today, there are over 900 million individuals suffering with some chronic diseases around the world. For this reason, e-health systems are being developed to design a better quality of life for patients. For instance, we have systems for detection of epilepsy, monitoring of vital signs, control of diabetes, among others. Deep learning has being an important technique embedded in these systems to predict and sort the data without the need of a 24-hour monitoring specialist. However, by combining e-health systems and deep learning techniques also brings several challenges that need to be overcome. Based on this context, this work-in-progress proposes an e-health system based on fog and cloud computing, using deep learning to predict epileptic seizures. We also present some research challenges for this implementation.
机译:慢性疾病呈指数增长。今天,全世界有超过9亿人患有某些慢性病。因此,正在开发电子保健系统,以为患者设计更好的生活质量。例如,我们拥有用于检测癫痫,监测生命体征,控制糖尿病等系统。深度学习已成为这些系统中嵌入的一项重要技术,无需24小时监控专家即可对数据进行预测和分类。但是,将电子卫生系统与深度学习技术相结合还带来了一些需要克服的挑战。在此背景下,这项正在进行的工作提出了一种基于雾和云计算的电子医疗系统,该系统使用深度学习来预测癫痫发作。我们还提出了针对此实现的一些研究挑战。

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