Deep Learning Ultra Low-Frequency Heart Rate Variability from raw ECG

概要：该项目将使用新的“机器学习”技术来分析心率变异性。如果有人说，“我的心脏跳动得像石头一样稳定”，他们可能需要被告知这是心脏病发作可能性增加的警告。与许多人的直觉相反，一颗健康的心脏不会像岩石一样稳定地跳动（岩石会跳动吗？）或节拍器，而是有不规则的跳动。这种自然和健康的心跳变化被称为“心率变异性”（HRV），在运动和医学中被广泛测量，但这种变异性的原因尚不清楚。在这项研究中，我们将开发新的软件来促进对这种不规则性的分析，并更好地了解其背后的生物学。人的心脏确实有一个内置的起搏器，在成年人的一生中，它以一种明显稳定的节奏跳动，但在这种规律的跳动之外，还有两种特征明显的潜意识机制，可以加速或减慢心跳。这两种调节机制的行为已被广泛研究，并导致心跳在一秒一秒或一分钟一分钟的时间范围内发生变化。然而，心跳也会在数小时或数天内发生变化，在过去，技术限制使得研究这种水平的细节变得非常困难，如果不是不可能的话。从本质上讲，人工选择和检查干净的ECG痕迹条是必要的，这对于非常大的数据集是不切实际的。以啮齿动物的心电图为例，这意味着每天要目测超过一百万次心跳！我们相信我们可以利用新的计算机和软件的发展来研究HRV的长期变化。具体来说，“深度学习”即所谓的人工网络，是现代人工智能（AI）的主要类型。这款软件类似于最新智能设备中允许Alexa或Siri回答口头命令的软件。在这个项目中，我们将开发这种类型的软件来协助远程心电图分析，并使用进一步的现代计算机模型来推断这种长期心率波动的生物学机制。我们的软件的应用将是广泛的，从人类和宠物的健康监测到运动人群的健身监测。由于心脏控制方式的变化是衰老的主要风险因素，因此分发此类软件将有利于健康老龄化议程。

Lay Summary:This project will use new "Machine Learning" technologies to analyse Heart Rate Variability.If someone says, "my heart beats steady as a rock", they probably need to be told that this is a warning of the increased likelihood of an impending heart attack. In contrast to many people's intuition, a healthy heart does not beat steadily like a rock (do rocks even beat?) or a metronome, but with an irregular beat. This natural and healthy variation between heartbeats is known as "Heart Rate Variability" (HRV) and is widely measured in sports and medicine, but the causes of the variability are not well understood. In this study, we will develop novel software to facilitate analysis of this irregularity and gain a better understanding of the biology behind it.The heart does have an inbuilt pacemaker that beats with an apparently steady rhythm throughout adult life, but on top of this regular beat, there are two well characterised subconscious mechanisms that can accelerate or decelerate the heart-beat. The behaviour of these two modulatory mechanisms has been extensively studied and causes the heartbeat to change in the second by second or minute by minute timeframe. However, the heartbeat also changes over the course of hours or days and technical limitations have made this very difficult, if not impossible to study at this level of detail in the past. Essentially, human selection and inspection of clean strips of ECG traces was necessary and this was impractical for very large datasets. In the case of rodent ECG traces, it would mean visually inspecting over a million heartbeats per day! We believe that we can make use of new computer and software developments to study the long-term changes in HRV. Specifically "deep learning" a so-called artificial network, and major type of modern artificial intelligence (AI). This is similar software to that allowing Alexa or Siri to answer verbal commands in the latest smart devices. In this project, we will develop this type of software to assist with long-range ECG analysis and use further modern computer models to infer the biological mechanisms underlying this long-term HRV.The applications for our software would be widespread, from health monitoring in people and pets and in fitness monitoring in sports people. Since changes in the way the heart is controlled are a major risk factor in ageing, distribution of such software will benefit the healthy ageing agenda.