APECS – Automatic Pressure Estimation through Cardiac Screening

APECS – 通过心脏筛查自动压力估算

• 批准号: 10001334
• 金额: $ 23.69万
• 依托单位: DIGITAL & FUTURE TECHNOLOGIES LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10001334
• 关键词: APECS; Automatic; Pressure; Estimation; through

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.

APEC 是一个利用人工智能探索心电图信号世界的机器学习项目。我们利用 Google Deepmind 的强大功能来探测、分析和模式匹配近 100,000 个可公开获取的心电图信号。APEC 的目标是确定心电图信号中是否存在隐藏的生物标记，从而帮助我们更好地了解心血管系统。这可以了解血管系统在动脉弹性、血液粘度等方面的运作方式。APEC 建立在公司迄今为止通过智能手表和健身设备等可穿戴技术建立多种生理参数的工作基础上。我们使用最新的人工智能思维来寻找从一种生理参数到另一种生理参数的模式，这些模式可能尚未记录。这使我们能够创建一个系统，可以连续记录个人的整体健康状况并进行趋势分析，从而进行真正的表现评分。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如何影响另一部分。APEC 的输出只是一种算法，但可以根据输入的心电图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，苹果公司在欧洲的 Apple Watch 4 可以记录过去几个月的心电图。我们都生活在一个忙碌的世界，由于需求不足，在医生手术中读取我们的生理数据变得越来越困难。我们相信，作为 APEC 项目一部分开展的工作将允许将其中一些表现生理标记记录在智能手表上并上传到云端，以提供趋势和分析，然后可供您的全科医生或家庭医生在线查看。我们实际上正在通过 APEC 努力优化我们获取并向临床医生提供生理表现数据的方式。我们还有很长的路要走，但 APEC 为生理监测世界提供了令人兴奋的视角，并使用机器学习来查看心电图信号（心电系统）是否存在我们目前不了解或什至意识到它们存在的某些方面。