HRV:Journal

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Home        Lab Members        Physiological Systems        Monitoring Parameters in the ICU        ECG        HRV        Clinician's Perspective        Cardiorespiratory Monitors        Signal Processing        Deliverables        Journal        Abbreviations       


This page describes the tasks completed by the team every week over the course of the project.

Project Plan

GanttChart.png
GanttChartColours.png


Figure 1: Gantt Chart of Project Timeline

Week 1

Objective

Background Research

Individual Tasks

Becky

  • ECG and set up iGEM wiki page

Calista

  • Cardiorespiratory monitors (features, functions and relationship with patient state)

Choi Wan

  • Physiological Systems

Eva

  • Heart rate variability (HRV) and slide template/design

Tara

  • Cardiorespiratory monitoring (who, what, where, why and how)

Presentation

Media: HRV_Background_1.pdf

Week 2

Objective

Research on currently available cardiorespiratory monitors and compare them

Individual tasks (companies to research on)

Becky

  • Omron and Getinge

Calista

  • Abbott, GE Healthcare and Medtronic

Choi Wan

  • Hill-Rom, Philips Healthcare and Boston Scientific

Eva

  • W.L.Gore & Associates and Lepu Medical Technology

Tara

  • Terumo and Edwards Lifesciences

Presentation

Media:HRV_CardioMonitors_2.pdf

Weeks 3-4

Objectives

  • Research into more specific aspects of cardiorespiratory monitors and compare them
  • Research from the perspective of clinicians

Individual Tasks

Becky

  • Home base of companies
  • Target users for each device
  • User interface for patients and HCP
  • Color code standard of alerts, interface, etc.
  • FDA and CE Mark classification, minimum requirements, testing criteria and approval

Calista

  • Cardiorespiratory parameters used for monitoring in the ITU and in relation to Covid
  • Real-life examples of cardiorespiratory monitoring in healthy and ill individuals
  • Siemens and their devices/technology
  • Strengths and weaknesses of each device (comparison)
  • Exercise monitoring (Omron, Apple Watch, Samsung) compared to clinical monitoring (accuracy standard)
  • Collaboration between clinicians and designers for product design

Choi Wan

  • Relationship and dependence between different parameters
  • Viewing mode of parameters in different devices (as graphs or values)
  • Criteria, differences between home monitoring or ICU monitoring
  • Market size and share of each major company
  • Invasive/non-invasive monitoring
  • Electronic patient records and their integration into hospitals/clinics

Tara

  • Cardiovascular conditions which are important to clinicians in the ICU
  • Perspective of clinician in selecting cardiorespiratory monitors
  • Minimal requirements of cardiorespiratory monitors in the ICU
  • Alarm systems in cardiorespiratory monitors

Eva

  • Off due to personal circumstances.

Presentation- Weeks 3&4

Media: HRV Clinician POV 3.pdf

Week 5

Objectives

  • Focus in on a few clinical conditions (COPD, heart attacks and sepsis)
    • What parameters are monitored and the clinical care pathways in hospitals for these conditions

Individual Tasks

Becky

  • Heart attack

Calista

  • COPD
  • Covid-19

Choi Wan

  • Sepsis

Eva

  • Sepsis

Tara

  • COPD

Presentation

Media: HRV_Conditions_in_ICU.pdf

Weeks 6-7

Objectives

  • Organise research into a clear structure on the Wiki
  • Integrate and connect the concepts and information gathered from previous weeks
  • Look at care pathways and case studies from journals

Individual Tasks

Update each section of the wiki with research from individual reports

Weeks 8-12

Objectives

  • Write individual reports and group planning report, first draft due by 28 Dec

Individual tasks

Becky

  • Report topic: Challenges of monitoring in the ICU
  • Group report section: Background, Preliminary Findings

Calista

  • Report topic: ECG
  • Group report section: Background, implementation plan/Gantt Chart

Choi Wan

  • Report topic: Sepsis
  • Group report section: Introduction, Background

Eva

  • Report topic: HRV
  • Group report section: Background, Conclusion

Tara

  • Report topic: COPD
  • Group report section: Backgorund, Risk assessment

Autumn Term Task Allocation

TaskAllocationAutumnTerm.png


Figure 2: Autumn Term Task Allocations

Week 13

Objectives

  • Signal processing methodologies/code
  • Find ECG datasets to analyse
  • Continue documenting on new findings

Tasks

  • Learn basic python and github to code collaboratively
  • Search for clean ECG data (not waveforms) to open using code
  • Update the wiki with new findings

Week 14

Objectives

  • SWOT analysis on HRV calculation methods
  • Signal Processing of ECGs (e.g. Finding RR peaks)

Tasks

  • Read papers on HRV analysis in different domains and compare them
  • Learn more about pandas for ECG signal processing and using stream for large datasets
  • Study and understand (existing) code(s) for HRV analysis
  • Note the sampling rate for each dataset and extract 15-20 beats for processing

Presentation

Media:Different_methods_to_extract_HRV.pdf

Week 15

Objectives

  • Signal Processing of ECGs
  • Pipeline of HRV derivation from ECGs
  • HRV in a clinical setting

Individual Tasks

Becky

  • Reading ECG from .dat files
  • Plotting ECGs
  • Summarise and create a flowchart of different ECG signal processing steps

Calista

  • Filtering and removing baseline drift from ECGs (preprocessing)

Choi Wan

  • Reading ECG from .dat files
  • Derivative approach

Eva

  • Contacting clinicians to conduct interview/survey on the use of HRV in clinical settings
  • Read papers on HRV analysis

Tara

  • Finding R peaks from ECG

Presentation

Media:ClinciansUse.pdf

Week 16

Objectives

  • Create a general code which can process ECGs in a standardised manner (e.g. use the same sampling frequency, interpolation, etc.)
  • R peak detection code
  • Utilising different filters for preprocessing of ECGs

Individual Tasks

Becky

  • Create artificial ECG/add noise to create new samples for testing
  • Create a diagram to outline all the steps involved in signal processing

Calista

  • Apply different filters to ECG
  • Test filters with noisier data

Choi Wan

  • Develop original R peak detection code instead of using available python modules

Eva

  • Continue looking at papers about HRV from a clincian's perspective
  • Follow-up on clinicians about questions/survey
  • Look at what the different bands in the power spectral density graph mean how the power spectral density relates to actual long-term conditions

Tara

  • Resample the function at 1000Hz
  • Look at how smooth interpolation function works

Presentation

The following contains feedback and results from a survey of healthcare professionals including doctors and nurses and an interview with a general practitioner. Media: ClinicianSurveyResults.pdf

Week 17

Objectives

  • Develop signal processing code of ECGs
  • Pipeline of HRV derivation from ECGs
  • Research more into HRV in a clinical setting

Individual Tasks

Becky

  • Reading ECG from .dat files
  • Plotting ECGs
  • Summarise and create a flowchart of different ECG signal processing steps

Calista

  • Filtering and removing baseline drift from ECGs (preprocessing)

Choi Wan

  • Reading ECG from .dat files
  • Derivative approach for peak detection

Eva

  • Contacting clinicians to conduct interview/survey on the use of HRV in clinical settings
  • Read papers on HRV analysis

Tara

  • Finding R peaks from ECG

Week 18

Objectives

  • Create a general code which can process ECGs in a standardised manner (e.g. use the same sampling frequency, interpolation, etc.)
  • R peak detection code
  • Utilising different filters for preprocessing of ECGs

Tasks

Becky

  • Create artificial ECG with baseline drift and 50Hz noise
  • Add noise to normal sinus rhythm ECG to create new samples for testing

Calista

  • Research types of noise commonly found in ECGs
  • Apply different filters to ECG for denoising

Choi Wan

  • R peak detection using derivative approach

Eva

  • Continue looking at papers about HRV from a clinician's perspective
  • Follow-up on clinicians about questions/survey
  • Look at what the different bands in the power spectral density graph mean how the power spectral density relates to actual long-term conditions

Tara

  • Resample the function at 1000Hz
  • Look at how smooth interpolation function works

Week 19

Objectives

  • Organise code, put different sections of code together
  • Presentation on clinician survey results
  • Start group report

Individual Tasks

Becky

  • Combine all sections of code into main python script
  • Organise git repo

Calista

  • Improve filtering
  • Organise git repo

Choi Wan

  • Improve R peak detection
  • Organise git repo

Eva

  • Summarise and present data from survey
  • Find out more about Kubios (leading HRV analysis software in the market)

Tara

  • Improve pre-processing
  • Make a rough outline of group report

Week 20

Objectives

  • Standardising plots (axes, labels, units, etc.) for displaying results
  • Run the code on ECGs of different conditions and artificial ECG
  • Group report

Individual Tasks

Becky

  • Standardise plot function in main script
  • Test code with artificial ECG
  • Writing first draft of group report
  • Organise git repo

Calista

  • Fix errors with filter
  • Writing first draft of group report
  • Organise git repo

Choi Wan

  • Plot RR intervals on a single graph
  • Writing first draft of group report
  • Organise git repo

Eva

  • In-depth analysis and graphs of survey results
  • Present data from GP survey
  • Writing first draft of group report

Tara

  • Organise git repo
  • Writing first draft of group report

Week 21

Objectives

  • Fine-tuning code
  • Finalise group report for submission

Tasks

  • Edit group report draft based on feedback
  • Add all data to appendices and make it neat
  • Fix references

Spring Term Task Allocation

Group Member Responsibilities
Tarane Subramaniam
  • Research into the physiological conditions most important in ICUs.
  • Research on ARDS and relation to HRV.
  • Taking minutes during meetings.
  • Writing the Input module of code.
  • Eva Tadros
  • Research into HRV.
  • Acting team leader.
  • Research into spectral analysis and correlating frequencies to clinical conditions.
  • Contacting and reviewing feedback on HRV from doctors, nurses and other health care providers.
  • Rebecca Vickery
  • Creating and updating the wiki.
  • Research into problems faced by clinicians associated with monitors.
  • Writing the Artificial ECG module of the code.
  • Streamlining the whole HRV code so module inputs and outputs flow.
  • Calista Yapeter
  • Research into the functions and use of cardiorespiratory monitoring in the ICU, the ECG, and its relationship to HRV.
  • Updating the wiki journal.
  • Writing the Filter ECG module of the code.
  • Choi Wan Yip
  • Research on interdependence of physiological parameter.
  • Research on sepsis and relation to HRV.
  • Communications between the team and supervisors outside of meetings.
  • Writing the Peak Detection module and the RR interval section of code.
  • Table 2: Individual tasks assigned during the spring term.


    Tasks shared among all members:

    • Researching and comparing cardiorespiratory monitors from different companies
    • Researching the extraction of HRV in different domains

    Weeks 22 - 30

    Easter Break and Final Exams

    Week 31

    Objectives

    • Review feedback from group report
    • Poster design and content

    Individual Tasks

    Becky

    • Methodology section of poster

    Calista

    • Conclusion section of poster

    Choi Wan

    • Results section of poster

    Eva

    • Individual presentation

    Tara

    • Introduction section of poster

    Week 32

    Objectives

    Poster presentation