HRV:Journal
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
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
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
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
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 | |
Eva Tadros | |
Rebecca Vickery | |
Calista Yapeter | |
Choi Wan Yip |
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