Name
Abstract | ||
|---|---|---|
Understanding of real world phenomena needs to incorporate the fact that observations on different scales each carry essential information. Multifractal formalism is tested if it can work as a robust estimator of monofractal properties when scaling interval is consistent with low-frequency (LF) band of power spectral analysis used in estimates of heart rate variability. Tests with fractional Brownian motions are performed to validate two popular multifractal methods: Wavelet Transform Modulus Maxima (WTMM) and Multifractal Detrended Fluctuation Analysis. Only WTMM method passes the tests when scaling is limited to LF band. Then WTMM method is applied in analysis of short-time control processes driving the heart rate. The significant difference is found between multifractal spectra describing healthy hearts and hearts suffering from left ventricle systolic dysfunction. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IMCSIT.2009.5352693 | Mragowo |
Keywords | Field | DocType |
Brownian motion,cardiology,fluctuations,medical signal processing,spectral analysis,wavelet transforms,fractional Brownian motion,heart rate variability,left ventricle systolic dysfunction,low frequency band,multifractal detrended fluctuation analysis,multifractal estimator,multifractal formalism,power spectral analysis,short time autonomic control,short time control process,wavelet transform modulus maxima | Computer science,Robust statistics,Detrended fluctuation analysis,Artificial intelligence,Wavelet transform,Statistical physics,Heart rate variability,Fractal,Statistics,Fractional Brownian motion,Multifractal system,Machine learning,Estimator | Conference |
ISBN | Citations | PageRank |
978-1-4244-5314-6 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danuta Makowiec | 1 | 6 | 4.74 |
| Gaaska, R. | 2 | 0 | 0.34 |
| Andrzej Rynkiewicz | 3 | 0 | 0.34 |
| Wdowczyk-Szulc, J. | 4 | 0 | 0.34 |