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

• 6. Kudaiberdieva G, Görenek B, Timuralp BJAJoCAKD. Heart rate variability as a predictor of sudden cardiac death. 2007;7

• 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

• 8. Lu G, Yang F, Taylor J, Stein JJJome, technology. A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. 2009;33(8):634-41

• 9. Guzzetti S, Rovere MTL, Pinna GD, et al. Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure. 2004;26(4):357-62

• 10. Thayer JF, Lane RDJBp. The role of vagal function in the risk for cardiovascular disease and mortality. 2007;74(2):224-42

•11. Berntson GG, Thomas Bigger Jr J, Eckberg DL, et al. Heart rate variability: origins, methods, and interpretive caveats. 1997;34(6):623-48

• 12. Evans S, Seidman LC, Tsao JC, Lung KC, Zeltzer LK, Naliboff BDJJopr. Heart rate variability as a biomarker for autonomic nervous system response differences between children with chronic pain and healthy control children. 2013;6:449

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• 15. Vagedes J, Gordon CM, Beutinger D, et al. Myofascial release in combination with trigger point therapy and deep breathing training improves low back pain. Fascia Research II, Basic Science and Implications for Conventional and Complementary Health Care: Elsevier, 2009:249.

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

• 19. Dobbin A, Dobbin J, Ross S, Graham C, Ford MJTjotRCoPoE. Randomised controlled trial of brief intervention with biofeedback and hypnotherapy in patients with refractory irritable bowel syndrome. 2013;43(1):15-23

• 20. Karavidas MK, Lehrer PM, Vaschillo E, et al. Preliminary results of an open label study of heart rate variability biofeedback for the treatment of major depression. 2007;32(1):19-30

• 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.

• 30. Georgiou K, Larentzakis AV, Khamis NN, Alsuhaibani GI, Alaska YA, Giallafos EJJFm. Can wearable devices accurately measure heart rate variability? A systematic review. 2018;60(1):7-20

• 31. Castaldo R, Melillo P, Bracale U, et al. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis.2015;18:370-77

• 32. Niskanen J-P, Tarvainen MP, Ranta-Aho PO, Karjalainen PAJCm, biomedicine pi. Software for advanced HRV analysis. 2004;76(1):73-81

• 33. Frequency-domain heart rate variability analysis performed by digital filters. 2010 Computing in Cardiology; 2010. IEEE.

• 34. Shaffer F, McCraty R, Zerr CLJFip. A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. 2014;5:1040

• 35. Tarvainen MP, Niskanen J-P, Lipponen JA, Ranta-Aho PO, Karjalainen PAJCm, biomedicine pi. Kubios HRV–heart rate variability analysis software. 2014;113(1):210-20

• 36. Singh B, Bharti NJIJoRSR. Software tools for heart rate variability analysis. 2015;6(4):3501-06

• 37. Rodríguez-Liñares L, Méndez AJ, Lado MJ, et al. An open source tool for heart rate variability spectral analysis. 2011;103(1):39-50

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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.