ME280 Fractional Order Mechanics

1. ME280 Fractional Order Mechanics

2. Fractional Analysis of Time Voltage Area of an EKG Presenter: MarwinKo

3. Motivation • Left ventricular hypertrophy (LVH) is when the cardiac muscle in your left ventricle (LV) thickens causing your heart to work harder • Time-voltage area was calculated detect (LVH) • Important because the LV is responsible for oxygenated blood being pumped throughout the whole body

4. Background Information Heart Rate (HR) – beats per minute Heart Rate Variability (HRV)– variation in the time interval between heartbeats

5. SA AV B-His

6. How can we get HRV data?

7. PhysioNet PhysioBank: “Archive of digital recordings of physiologic signals…and other biomedical data.” • Obtained 3 sets of data • MIT-BIH Normal Sinus Rhythm Database • MIT-BIH Arrhythmia Database • MIT-BIH Sudden Cardiac Death Database PhysioToolkit: “Library of software for physiologic signal processing and analysis…” • Multiple errors occurred in MATLAB when running PhysioToolKit for MATLAB. • Used “rddata.m” to convert data files into readable data for MATLAB also includes an embedded QRS detector

8. Code Outline • Run EKG data • Run for one minute per data set • Show area of interest • Using -0.8mV as a baseline (resting electric potential) • Calculate area per interval • Manually find peaks (data sets of about 100 data points) • Run Hurst Estimators • Aggregated Variance Method • R/S Method

9. Code Outline • Run EKG data • Run for one minute per data set

10. Code Outline • Show area of interest • Using -0.8mV as a baseline (resting electric potential)

11. Code Outline • Calculate area per interval • Manually find peaks (each data set approximately 100 data points) R R R R Time Series!

12. Code Outline • Run Hurst Estimators (the other Methods did not work well with the small data set) • R/S Method • Aggregated Variance Method

13. Hurst Estimator • Heart rate variability is a long range dependent time series! • Do not have that much data to work with… • Using the R-R areas as a time series data set, thus avoiding periodic data set which the Hurst estimator is sensitive to • H<0.5  Volatile process (non-persistent) • H=0.5  Non-correlated process (randomness) • H>0.5  Corresponds to a trending (persistent) process

14. Data Utilized • One minute long portions were used • Three EKG readings were used: • Arrhythmia (92 beats/min, 91 R-R interval) • Sudden Cardiac Death (97 beats/min, 91 R-R interval) • Normal Sinus Rhythm (89 beats/min, 91 R-R interval) • Normal Sinus Rhythm data would not compile via MATLAB code provided by PhysioNet • As a result, I used a patient from the arrhythmia data set who exhibited normal sinus rhythm episodes.

15. Data Utilized Normal Arrhythmia Sudden Cardiac Death

16. Normal Sinus Rhythm H=0.7393 H=0.4746

17. Arrhythmia H=0.8873 H=0.8726

18. Sudden Cardiac Death H=0.6878 H=0.6194

19. Hurst Estimator:Aggregated Variance Method Normal Arrhythmia Sudden Death H=0.8726 H=0.4746 H=0.6194 H<0.5  Volatile process (non-persistent) H=0.5  Non-correlated process (randomness) H>0.5  Corresponds to a trending (persistent) process H>0.5  Corresponds to a trending (persistent) process

20. Hurst Estimator:R/S Normal Arrhythmia Sudden Death H=0.8873 H=0.7393 H=0.6878 H>0.5  Corresponds to a trending (persistent) process H>0.5  Corresponds to a trending (persistent) process H>0.5  Corresponds to a trending (persistent) process

21. Conclusion • Normal Sinus Rhythm • Inconclusive results, need to run real “Normal Sinus Rhythm” EKG data. • Arrhythmia • Both results have H>0.5 • Trending towards the tachycardia (high heart rate) • Sudden Cardiac Death • Both results have H>0.5 • Trending towards the erratic EKG signal • Need more data for more conclusive results!

22. Future Plans • Find a better way to obtain the data sets • “R” peaks were all manually found using MATLAB figures • Trouble shoot PhysioNet data converter code • Test out other Hurst Estimators, once bigger data sets are available

23. Thanks & Appreciation • Dr. Chen • Zhou Li • Taizhi Lyu

24. Questions?

25. References • Goldberger AL, AmaralLAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.Circulation101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13). • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.Circulation101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13). • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.Circulation101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13). • Boron, Walter F., and Emile L. Boulpaep. Medical Physiology. 2nd ed. Philadelphia, PA: Saunders, 2012. Print. • Rose, 0. "Estimation of the Hurst Parameter of Long-Range Dependent Time Series."University of Wurzburg Institute of Computer Science Research (1996): 1-15. Web. 22 Nov. 2013. • Markovic, D., and M. Koch. "Sensitivity of Hurst Parameter Estimation to Periodic Signals in Time Series and Filtering Approaches." Geophysical Research Letters (2005): 1-10. Web. 15 Nov. 2013. • Leite, A., AP Silva, S. Gouveia, J. Carvalho, and O. Costa. "Long-Range Dependence inHeart Rate Variability Data: ARFIMA Modelling vsDetrended Fluctuation Analysis."IEEEXploreDigital Library (2007): 1-4. Print. • http://www.mathworks.com/matlabcentral/fileexchange/19148-hurst-parameter-estimate • http://www.mathworks.com/matlabcentral/fileexchange/13188-shade-area-between-two-curves • http://www.myschoolhouse.com/courses/O/1/94.asp