APECS – Automatic Pressure Estimation through Cardiac Screening

APEC是一个机器学习项目，使用人工智能来探索ECG信号的世界。我们使用Google的Deepmind来探测，分析和模式匹配近100，000个公开访问的ECG信号。APEC的目的是确定ECG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如何在动脉弹性，血液粘度等方面运作。APEC建立在公司迄今为止的工作基础上，通过智能手表和健身位等可穿戴技术建立多个生理参数。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，这可能是没有记录的。这使我们能够创建一个系统，在该系统中，可以连续记录个人的整体健康状况并进行趋势分析，从而实现真正的性能评分。使用F1 Motorsport的数据可视化软件，我们将APEC正在处理的数据与实现系统联系起来，使我们能够可视化生理系统的一部分如何影响另一部分。APEC的输出只是一个算法，而是可以根据输入的ECG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，苹果公司启用了Apple Watch 4，在欧洲，我们都生活在一个忙碌的世界里，去看医生，在医生的手术中进行我们的生理读数由于供不应求，越来越难以实现。我们相信，作为APEC项目的一部分所开展的工作将允许将这些生理指标中的一些记录在智能手表上，并上传到云端，以提供趋势和分析，然后可以由您的全科医生或家庭医生在线审查。我们正在通过APEC有效地优化我们获取和向临床医生提供生理性能数据的方式。我们还有很长的路要走，但APEC提供了一个令人兴奋的观察生理监测的世界，并使用机器学习来了解ECG信号的某些方面-心脏电气系统-我们目前还不了解，甚至没有意识到它们在那里。

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.