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An Analysis Of Heart Rate Variabilities Using Open Source
Software A Master’s Thesis Presentation
by Tanzila ZamanCarolina Health Informatics Program
Fall 2019
RTI International Internship Logistics
TimelineBeginning- June 10, 2019Research Brief- Mid July, 2019Poster Presentation- August 12, 2019
Deliverables• Poster Presentation• Research Brief• Weekly meeting• Lunch Talk • RTI-wide Mandatory Workshops on Professional
Development
An Analysis Of Heart Rate Variability (HRV) Using An Open Source Software
Project
Growth And Adoption Of
Wearable Device
• As of June, 2018, 5% of the technologies have been formally validated.
• Biofeedback received helps in improving outcomes using health and fitness technology.
• Most common biofeedback present in all technology is the heart rate.
Rise Of Popularity Of
HRV
Phases Of HRV Project
EXPLORING HEART RATE VARIABILITY
DATA GENERATION LIT. REVIEW OF OPEN SOURCE SOFTWARES
RESULTS FROM KUBIOS DATA ANALYSIS
CONCLUSION AND LIMITATIONS
Phases of HRV Project
EXPLORING HEART RATE VARIABILITY (HRV)
Sympathetic Nervous
System and Parasympathetic Nervous System
Brake = Parasympathetic
Gas = Sympathetic
Understanding Heart Rate or R-R or interbeat (IBI) variation
Variation among successive heartbeats is defined as Heart Rate Variability (HRV)
R R
Importance of HRV
• Indicator of Autonomic Nervous System (ANS) Activity
• Pre-Diagnostic Tool for the following:
• Cardiac abnormalities• Treating Asthma• Functional Gastrointestinal
Disorders• Psychological Disorders
• Biofeedback from HRV is used for Performance Enhancement
Phases of HRV project EXPLORING HEART
RATE VARIABILITY (HRV)
DATA GENERATION
Data Acquisition
With rise of new technology data is gathered using wearable devices that contains photoplethysmogram (PPG) Sensors. This allows patient to be mobile and data is collected continuously.
Conventional way of measuring HR data consisted of multiple electrodes of the electrocardiogram (ECG) that placed on patience chest
Empatica E4 Wristband
• Unhindered Monitoring• Precise Data Collection• Tags Physiological Events• Easy Access (CSV files)• Readily available at RTI
Phases of HRV project
EXPLORING HEART RATE VARIABILITY
(HRV)
DATA GENERATION
LITERATURE REVIEWS OF OPEN
SOURCE SOFTWARE
Literature Search Process
Word cloud shows keywords used to search literature reviews in various search engines. Use of the keyword ‘HRV’ generated maximum number of articles.
Literature Review Process
Literature reviews using search engines PubMed, Google Scholar, UNC Library Catalog
Output Data Format Users Prog.
Language Operating
System
Inclusion Criteria:Paper in scientific journal
•Free HRV-Softwares•Full English text only
Published 2010 and beyond
HRV Softwares
Software Operating System
Output Data Format
ProgrammingLanguage
User Total No. Of Lit. Reviews
KubiosLinus Windows
Mac OS XMAT files
ASCII files.pdf file
MATLAB Any user 5838
RHRVUNIX
WindwsMacOS
ASCII filesWFDB R
Physicians with concrete clinical studies in mind 418
gHRV
LinuxwindowsMacOS
.txt filesPython
Researchers, clinical
professional with no programming
skills
193
Phases of HRV project
EXPLORING HEART RATE VARIABILITY (HRV)
DATA GENERATION
LITERATURE REVIEWS OF OPEN SOURCE SOFTWARE
RESULTS FROM ANALYSIS USING KUBIOS
Time Domain
Results Generated Using Kubios
Frequency Domain
Non-Linear Domain
Time Domain
• Statistics of R-R interval measuring standard deviation of R-R
• Parameters- SDNN (std. dev of N-N), RMSSD(root mean sq. successive dif.) are measured in ms
• Indicator of Parasympathetic Activity
• Lower than standard value is not considered favorable
• Requires 24 hours aka “gold standard” of continuous data collection
Frequency Domain
• Most important domain for short-term analysis of HRV
• Power/Energy calculated from R-R interval within specific frequency bands (PSD-power spectrum density)
• Parameters - High Frequency (HF) 0.15-0.40Hz is influenced by breathing from 9-24 bpm. Low Frequency (LF) is influenced by 3-9 bpm. HF, LF are measure in normalized units(nu) for comparison.
• Low LF/HF ratio Parasympathetic Activity
• High LF/HF ratio Sympathetic Activity• Data may be collected for both long-
term 24 hours or short-term of 5 mins.
Phases Of HRV Project
EXPLORING HEART RATE VARIABILITY
DATA GENERATION LIT. REVIEW OF OPEN SOURCE SOFTWARES
RESULTS FROM KUBIOS DATA ANALYSIS
CONCLUSION AND LIMITATIONS
Limitations• Very small sample size (n=2)• <24 hours of data collection• Single software for data acquisition• Lack of short-term reference values
Recommendations For Future Study
Recommendation• Increase sample size a min of 30 participants• Collect data for 24 hours straight• Compare and contrast data across various wearable devices• Analyze data using other softwares
Conclusion
Through this preliminary study I learned the following: Importance of HRV Kubios is implemented and most used open source software for analyzing HRVMetrics in HRV and its importance Data generated and analyzed via Kubios Result shows dominance of SNS
References • https://www.emarketer.com/content/older-americans-drive-growth-of-wearables
• 1. Tsuda TJJoH, Cardiology. The Importance of Cardiovascular Research: What are we looking for? 2014;1(1):0-0
• 2. Dick SA, Epelman SJCr. Chronic heart failure and inflammation: what do we really know? 2016;119(1):159-76
• 3. Huikuri HV, Mäkikallio THJAN. Heart rate variability in ischemic heart disease. 2001;90(1-2):95-101
• 4. Camm AJ, Malik M, Bigger JT, et al. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. 1996
• 5. Hohnloser SH, Klingenheben T, Zabel M, Li YGJP, electrophysiology c. Heart rate variability used as an arrhythmia risk stratifier after myocardial infarction. 1997;20(10):2594-601
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• 7. Sandercock GRH, Brodie DAJP, electrophysiology c. The role of heart rate variability in prognosis for different modes of death in chronic heart failure. 2006;29(8):892-904
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References Cont.• 16. Gevirtz RJB. The promise of heart rate variability biofeedback: Evidence-based applications. 2013;41(3):110-20
• 17. Sowder E, Gevirtz R, Shapiro W, Ebert CJAp, biofeedback. Restoration of vagal tone: a possible mechanism for functional abdominal pain. 2010;35(3):199-206
• 18. Slutsker B, Konichezky A, Gothelf DJP, Health, Medicine. Breaking the cycle: cognitive behavioral therapy and biofeedback training in a case of cyclic vomiting syndrome. 2010;15(6):625-31
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• 21. Tan G, Dao TK, Farmer L, Sutherland RJ, Gevirtz RJAp, biofeedback. Heart rate variability (HRV) and posttraumatic stress disorder (PTSD): a pilot study. 2011;36(1):27-35
• 22. McCraty R, Atkinson M, Lipsenthal L, Arguelles LJAP, Biofeedback. New hope for correctional officers: an innovative program for reducing stress and health risks.2009;34(4):251
• 23. McLay RN, Spira JLJAp, biofeedback. Use of a portable biofeedback device to improve insomnia in a combat zone, a case report. 2009;34(4):319
• 24. Strack B, Gevirtz RJB, psychology nais. Getting to the heart of the matter: Heart rate variability biofeedback for enhanced performance. 2011:145-74
• 25. Lagos L, Vaschillo E, Vaschillo B, Lehrer P, Bates M, Pandina RJB. Heart rate variability biofeedback as a strategy for dealing with competitive anxiety: A case study. 2008;36(3):109
• 26. Gruzelier JH, Thompson T, Redding E, Brandt R, Steffert TJIJoP. Application of alpha/theta neurofeedback and heart rate variability training to young contemporary dancers: State anxiety and creativity. 2014;93(1):105-11
• 27. Raymond J, Sajid I, Parkinson LA, Gruzelier JHJAP, Biofeedback. Biofeedback and dance performance: A preliminary investigation. 2005;30(1):65-73
• 28. Wells R, Outhred T, Heathers JA, Quintana DS, Kemp AHJPo. Matter over mind: a randomised-controlled trial of single-session biofeedback training on performance anxiety and heart rate variability in musicians. 2012;7(10):e46597
• 29. Wearable heart rate monitor using photoplethysmography for motion. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES); 2014. IEEE.
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• 32. Niskanen J-P, Tarvainen MP, Ranta-Aho PO, Karjalainen PAJCm, biomedicine pi. Software for advanced HRV analysis. 2004;76(1):73-81
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References Cont.
Acknowledgement- RTI
Dr. Robert Furberg (Mentor) Ms. Alexa Ortiz Dr. Stephanie Eckman Ms.Jacqueline Bagwell
Acknowledgement- UNC
Dr. Saif Khairat (Advisor) Dr. Leah Townsend
Acknowledgement- UNC
Ms. Ruiz Mariell Ms. Lindsay Womack Ms. Kelly March
Thank You!Questions
Additional Slides
Methods
• HR data collected from three female were ran through Kubios softwares.
• Metrices including LF, HF, SD1 and SD2 were measured
• Excel to run t-test and correlation were calculated.
Figure show three graphs of HRV result using Kubios from three females
Subject 1 Subject 2 Subject 3
Result
1.15
0.77
1.090.95 0.99
1.24
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Subject 1 Subject 2 Subject 3
Comparison of LF/HF ratio to SD1/SD2 ratio
LF/HF SD1/SD2
• Correlation of LF/HF to SD1/SD2 = 0.22• Welch Two Sample t-test P-value= 0.71, t = - 0.40• Fisher Test P-value= 1
• Findings: The results show LF/HF ratio and SD1/SD2 ratio are not significantly correlated; however, p-value shows no significant difference between these ratios.
• Conclusion: Our results shows both ratios shows similar results i.e. SNS activity.
• Limitations: Very small sample size, biological factors have not been taken into consideration.