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.

摘要摘要：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：“我的心脏稳定为岩石”，则可能需要告诉他们这是对即将发生心脏病发作的可能性增加的警告。与许多人的直觉相反，健康的心脏不会像岩石那样稳步跳动（甚至摇滚？）或节拍器，而是有不规则的节奏。心跳之间的这种自然和健康的变化被称为“心率变异性”（HRV），在运动和医学中广泛测量，但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，以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。心脏确实有一个内置的起搏器，在整个成年人的整个生活中都以明显的稳定节奏来击败，但除了这种常规的节奏之外，还有两种富有特征的潜意识机制，可以加速或衰减心脏 - 心脏脱发。这两种调节机制的行为已经进行了广泛的研究，并导致心跳在第二或分钟的第二分钟内改变。但是，心跳在几个小时或几天的过程中也会发生变化，而且技术限制使这一局限性变得非常困难，即使不是过去的细节级别。本质上，需要人类的选择和检查清洁的心电图痕迹条，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，这意味着每天在视觉上检查超过一百万的心跳！我们认为，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。特别是“深度学习”所谓的人造网络，以及主要的现代人工智能（AI）。这与允许Alexa或Siri可以在最新智能设备中回答口头命令的软件相似。在该项目中，我们将开发这种类型的软件来协助长期ECG分析，并使用进一步的现代计算机模型来推断这种长期HRV的生物学机制。我们软件的应用程序将广泛扩展，从人，宠物中的健康监测以及体育人员的健身监测。由于控制心脏的方式的变化是衰老的主要危险因素，因此该软件的分布将使健康的衰老议程受益。

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