Award details

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

Reference	BB/S008136/1
Principal Investigator / Supervisor	Professor Richard Barrett-Jolley
Co-Investigators / Co-Supervisors	Dr Gabriela Czanner, Professor Yalin Zheng
Institution	University of Liverpool
Department	Institute of Ageing and Chronic Disease
Funding type	Research
Value (£)	229,821
Status	Current
Type	Research Grant
Start date	14/07/2019
End date	18/08/2023
Duration	49 months

Abstract

Heart rate variability (HRV) is a well-recognised phenomenon in people and other animals and the relatively high-frequency components, in the range 0.015 to 4Hz (termed very low-frequency vLF, low-frequency LF, and high frequency, HF) are used frequently and there is evidence that they derive from the activity of the autonomic nervous system. In reality, these are all rather high-frequency components in a physiological context, with time periods of even so-called "low frequency" components stretching to only a minute or so. Lower frequency components, ultra-low frequency, (uLF or those frequencies one might call sub-ultra-low frequency or frequencies in the microhertz (muHz)) range are have rarely been studies and the origins of this variability are completely unknown. Part of the reason for this knowledge gap is the fact that very good quality ECG signals are necessary for HRV and traces are typically inspected and selected by eye before conducting the analysis. This is infeasible for long-range signals and introduces significant experimental bias. Furthermore, long-term recording requires the subject to be freely moving and incurring "noise" dislodging electrodes etc. Commercial apparatus for such analysis disguise missing data and the user never knows, but the HRV signal recorded would become scientifically worthless. The past solution to the long-term analysis of HRV is to pick and analyse short segments taken over the course of a longer-term experiment, or session. The bias inherent in such workflows is obvious. Our proposal is to use deep learning to automatically process ECG signals to create frequency periodograms with robust sub-ultra low-frequency powers, and then to simulate the biology of the muHz frequency powers with a Bayesian model. We hypothesis the circadian rhythm contributes to the microhertz frequency variability of HRV and we will test this using telemetric recording of HRV from rodents.

Summary

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.

Impact Summary

Further details INCLUDING timelines included in the Pathways to Impact attachment. *Industry* When we have robust prototype deep learning models available we will approach our current collaborators Medtronic with regards to adapting their analysis pipeline to include deep learning; we have already spoken to them about data exchange, but we cannot offer validated prototypes until this project is completed. We also have good relations with Millar Telemetry and CED and have discussed collaboration; CED stated they would be happy to incorporate our models once they are distribution ready. *Training* Training is a big part of this; our growing group will train many undergraduate students (RBJ had 5 undergrad project student this year) in far more mathematical and statistical projects than is typical in biology. The value of this project to the undergraduate skills base should not be overlooked since both physiology and mathematical biology are listed as endangered biological skills by the BBSRC. Our PDRA will be further trained in both computational analyses with deep learning and Bayesian inference to compliment her existing skills in cardiovascular biology. *3Rs* This project will develop technology to significantly improve the amount of data that can be extracted from animal experiments using telemetric recording and so if the project is funded, we will be able to approach the NC3Rs with a proposal to deliver HRV analysis to animal units throughout the country. Telemetric recording will reduce severity (refinement) and reduce the numbers of animals needed (reduction), we also hope that our HRV model may allow replacement of animals in some mechanistic studies. *Dissemination* We will disseminate through the RBJ conceived and created Meet-The-Scientists event in Liverpool World Museum. We will demonstrate heart rate variability to museum visitors, which largely, but not exclusively consist of families. We will invite members of the public to come along and joint in some science! We will equip participants with HR monitors and explain about HRV and its pitfalls and (with adults) the limitations of the consumer devices they are most probably wearing. We will also demonstrate our machine learning detection methods to adults and older children. For biological scientists, we will host a workshop at the Physiological Society summer annual meeting and for data-scientists and hobbyists, we will run a Kaggle machine-learning competition.

Committee	Research Committee C (Genes, development and STEM approaches to biology)
Research Topics	Systems Biology, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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