APECS – Automatic Pressure Estimation through Cardiac Screening

APEC是一个利用人工智能探测心电信号世界的机器学习项目。我们使用Google的DeepMind的能力来探测、分析和模式匹配近10万个公开可访问的心电信号。APEC的目标是确定心电信号中是否存在隐藏的生物标记，以帮助我们更好地了解心血管系统。这可能是了解血管系统是如何在动脉弹性、血液粘度等方面运行的。APEC建立在两家公司迄今通过智能手表和Fit Bits等可穿戴技术建立多个生理参数的基础上。我们使用最新的人工智能思维来寻找从一个生理参数到另一个生理参数的模式，这些模式可能还没有记录下来。这使我们能够创建一个系统，在这个系统中，可以连续地记录个人的整个健康状况，并对其进行趋势分析，从而进行真正的性能评分。使用F1赛车运动的数据可视化软件，我们将APEC正在处理的数据链接到一个实现系统，使我们能够可视化生理系统的一个部分是如何影响另一个部分的。APEC的输出只是一个算法，但它可以根据输入的心电系统数据来预测生理表现。这是新颖的，非常适用于智能手表市场，苹果支持Apple Watch 4，在欧洲，记录过去几个月的心电。我们都生活在一个繁忙的世界里，由于供不应求，要在医生的手术中看医生并获得生理读数变得越来越困难。我们相信，作为APEC项目的一部分进行的工作将允许将其中一些性能生理标记记录在智能手表上并上传到云中，以提供趋势和分析，然后您的全科医生或家庭医生可以在线查看。我们正在通过APEC努力优化我们获取生理性能数据并将其呈现给临床医生的方式。我们还有很长的路要走，但APEC提供了一个令人兴奋的关于生理监测世界的展望，并使用机器学习来查看是否存在我们目前不了解甚至意识到的心电信号的某些方面。

APEC is a Machine Learning Project using Artificial Intelligence to probe the world of ECG signals.We use the power of Google's Deepmind to probe, analyse and pattern match almost 100,000 publicly accessible ECG signals.The aims of APEC are to establish if there are hidden bio-markers within the ECG signals that can help us understand the cardiovascular system better. This could be understanding how the vascular system is operating in terms of arterial elasticity, blood viscosity etc.APEC builds on the companies works to date of establishing multiple physiological parameters from wearable technologies such as smart-watches and fit bits.We use the latest Artificial Intelligence thinking to look for patterns from one physiological parameter to another, which may not have been recorded. This enables us to create a system where the whole health of the individual may be recorded continuously and trended allowing for true performance scoring to take place.Using data visualisation software from F1 Motorsport we link the data that APEC is processing to a realisation system allowing us to visualise how one part of the physiological system is affecting the other.The outputs of APEC are simply an algorithm, but one that can see predict physiological performance based on the ECG system data being fed into it.This is novel and is very applicable to the smart-watch market with Apple enabling the Apple Watch 4, in Europe, to record ECG in the last few months.We all live in a busy world where getting to see a doctor and having readings on our physiology performed in the doctors surgery are getting harder to achieve owing to demand out stripping supply. We believe that the works undertaken as part of the APEC project will allow for some of these performance physiological markers to be recorded on smart-watches and uploaded to the cloud to provide trending and analysis that can then be reviewed online by your GP or family doctor.We are in effect through APEC working to optimise the way in which we acquire and present physiological performance data to our clinicians. We still have a long way to go, but APEC provides an exciting look into the world of physiological monitoring and using Machine Learning to see if there are aspects of the ECG signal - the hearts electrical system - that we currently do not understand or even realise that they are there.