Activities
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Seminars
| Seminar on Theoretical and Computational Physics:
| | Title | Classifying Self-terminating Ventricular Fibrillation by Bivariate Data Analysis
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| Speaker | Dr. Le Duy Manh
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| Affiliation | Center for Computational Physics, Institute of Physics, VAST
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| Date | Tuesday, 09-12-2014
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| Time | 10:00 AM
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| Location | Meeting room, first floor, Institute of physics, 10 Dao Tan, Ba Dinh, Hanoi
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| Abstract | Heart is a complex dynamical system that contains many components worked rhythmically in a coordinated manner to produce rhythmic activity for effectively pumping blood, feeding activities of the whole living body with nutrition and oxygen. Under fast electrical pacing, heart shows rich dynamical behaviors due to its instability, such as alternans, tachycardia and fibrillation. Ventricular fibrillation (VF) is an extremely serious arrhythmia which is known to be the major cause of sudden cardiac death, and thus the research to understand its mechanism as well as clinical treatments is very important. In our study, VF in isolated rat hearts perfused in the Langendorff system is induced by fast electrical pacing. Electrical signals from right atrium (a site very closed to sinoatrial node) and left ventricle are recorded simultaneously. We find that when there is strong component of ventricular signal detected in the atria one during VF, the induced VF is usually not self-terminating. Quantitative criteria for the prediction of self-terminating VF are proposed based on the analysis of bivariate time series (atrial and ventricular signals) by the cross-wavelet and cross-Fourier power spectra methods. The success rate of our prediction is about 80-90%. Our findings suggest that a heart under VF can recover its sinus rhythm only when the sinoatrial node of the heart is not under strong influence of the VF from its ventricle.
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