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

根据原始心电图深度学习超低频心率变异

• 批准号: BB/S008136/1
• 负责人: Richard Barrett-Jolley
• 金额: $ 31.34万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS008136%2F1
• 关键词: Deep; Learning; Ultra; Low; Frequency

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.

外行摘要：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“我的心跳稳定如磐石”，他们可能需要被告知这是一个警告，表明心率变异性的可能性增加。即将发作的心脏病。与许多人的直觉相反，健康的心脏并不像石头（石头甚至会跳动吗？）或节拍器那样稳定地跳动，而是不规则地跳动。这种心跳之间自然且健康的变化被称为“心率变异性”(HRV)，在运动和医学领域得到广泛测量，但这种变异的原因尚不清楚。在这项研究中，我们将开发新颖的软件，以方便分析这种不规则性，并更好地了解其背后的生物学原理。心脏确实有一个内置起搏器，在整个成年生活中，它以明显稳定的节奏跳动，但除此之外心跳，有两种特征明确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，并导致心跳在每秒或每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的过程中发生变化，而技术限制使得在过去进行如此详细的研究变得非常困难，甚至不可能。本质上，人工选择和检查干净的心电图痕迹条是必要的，但这对于非常大的数据集来说是不切实际的。就啮齿动物心电图痕迹而言，这意味着每天要目视检查超过一百万次心跳！我们相信，我们可以利用新的计算机和软件开发来研究 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。该软件与最新智能设备中允许 Alexa 或 Siri 应答口头命令的软件类似。在这个项目中，我们将开发此类软件来协助长期心电图分析，并使用进一步的现代计算机模型来推断长期 HRV 背后的生物机制。我们软件的应用将会广泛，从健康监测到人和宠物以及运动员的健康监测。由于心脏控制方式的变化是衰老的主要风险因素，因此此类软件的分发将有利于健康老龄化议程。

项目成果

DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.

• DOI: 10.1371/journal.pone.0267452
• 发表时间: 2022
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ball STM;Celik N;Sayari E;Abdul Kadir L;O'Brien F;Barrett-Jolley R
• 通讯作者: Barrett-Jolley R

Discriminant Analysis of Principle Component analyses of Physiological Data

生理数据主成分分析的判别分析

• DOI: 10.1101/2020.01.09.899898
• 发表时间: 2020
• 作者: Haidar O

Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data

Deep-Channel 使用深度神经网络从膜片钳数据中检测单分子事件

• DOI: 10.1101/767418
• 发表时间: 2019
• 作者: Celik N

An Ion Channel Event Detector using a Recurrent Convolutional Neural Network

使用循环卷积神经网络的离子通道事件检测器

• 发表时间: 2019
• 作者: Celik

DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks

• DOI: 10.1101/2020.06.25.171918
• 发表时间: 2020-06
• 期刊: PLoS ONE
• 影响因子: 3.7
• 作者: Numan Celik;Sam T M Ball;E. Sayari;Lina Abdul Kadir;Fiona O’Brien;R. Barrett-Jolley