BRI Poster - Prateek Mathur v1.1

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To develop a classification method used to distinguish cardiac arrhythmias from normal sinus rhythm.Fractal properties of IBI can be used to classify cardiac data and aid in diagnosing heart conditions.

Multifractal Detrended Fluctuation Analysis (MFDFA)RR interval time series is segmented into a moving window at different time scales to create n partitions.

Time- and scale-dependent root-mean-square (RMS) values are found using a linear fit applied at each window and partition.

Local scaling exponents (Holder exponents) were determined by finding the slope of a log-log plot of the RMS values and scale at each time point.Symbolic Representation (Bag-Of-Words)From the newly acquired Holder time series, eachpoint is assigned to a letter defined by its location on the series probability density function. [5]

Frequency of occurrence is recorded for each permutation of letter vectors (words) of specifiedlengths.It has been shown that signal complexitycan be used as an indicator fordisease and aging. [1]Cardiac inter-beat interval fluctuations (IBI) signal loses its complexity when the heart is in adiseased state, such as cardiac arrhythmias. [2]One measure of complexity is throughfractal properties.

Fractals are patterns that exhibit self similarity over different time scales.Simple fractal signals can be described by a singlescaling exponent (monofractal). Complex signals exhibit a spectrum of exponents (multifractal).[1] A. Goldberger, L. Amaral, J. Hausdorff, P. Ivanov, C. Peng and H. Stanley, "Fractal dynamics in physiology: Alterations with disease and aging, Proceedings of the National Academy of Sciences, vol. 99, no. 1, pp. 2466-2472, 2002.

[2] M. Saeed, "Fractals Analysis of Cardiac Arrhythmias", The Scientific World JOURNAL, vol. 5, pp. 691-701, 2005.

[3] A. Goldberger, "Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside", The Lancet, vol. 347, no. 9011, pp. 1312-1314, 1996.

[4] E. A. F. Ilhen, "Introduction to Muiltifractal Detrended Fluctuation Analysis in Matlab, Frontiers in Physiology, vol. 3, 2012.

[5] J. Lin and Y. Li, "Finding Structural Similarity in Time Series Using Bag-of-Patterns Representation," Springer-Verlag, Berlin, 2009.Classification of Cardiac Data based on Multifractal Feature ExtractionPrateek Mathur1,2, Hamidreza Saghir MSc1,3, Tom Chau, PhD, P. Eng1,3,41Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital; 2Department of Electrical and Computer Engineering, McMaster University;3Institute of Biomaterials and Biomedical Engineering, University of Toronto; 4Toronto Rehabilitation InstituteSpecial thanks to the Ward Family Summer Student Research Program, University of Toronto IBBME USRP, and NSERC USRA for funding and facilitating this summers research.Thanks to Dr. Tom Chau, Hamidreza Saghir, Ka Lun Tam, and the rest of the PRISM Lab for their insight and advice.

BackgroundReferences

Results show MFDFA combined with symbolicrepresentation is a viable method to classify cardiac data.These techniques have potential as alternativediagnosis methods for cardiac conditions, and canbe used to assess a patients risk of cardiac event.Next steps involve developing classificationmethods for multiple types of arrhythmias and other cardiac conditions, allowing for more targeted treatment.Conclusions/Future WorkMethods

ObjectivesECG recordings of 48 patients from MIT-BIH Arrhythmia database and 18 patients from MIT-BIH Normal Sinus Rhythm database.[3]

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a3-6 letter alphabets were tested and classified for word lengths of 2-6 letters.For each word length tested, a table of wordoccurrences of each permutation is fed into a binary linear classifier.Using 10-fold cross validation and word counts as features, signals are classified as either normal sinus rhythm or arrhythmia.Highest average classification accuracy was obtained with a 6-letter alphabet (91.5%).Classification/Results6-Letter Alphabet Word LengthAccuracySensitivitySpecificity288.6 %89.7 %86.1 %394.6 %95.8 %89.2 %492.0 %95.4 %86.7 %592.7 %94.9 %86.7 %689.7 %91.1 %87.0 %

[4][4]DatasetHypothesisAcknowledgements

RR IntervalECG Time SeriesRR IntervalRR Interval

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