Literature DB >> 3396335

Fractals and the analysis of waveforms.

M J Katz1.   

Abstract

Waveforms are planar curves--ordered collections of (x, y) point pairs--where the x values increase monotonically. One technique for numerically classifying waveforms assesses their fractal dimensionality, D. For waveforms: D = log(n)/(log(n) + log(d/L], with n = number of steps in the waveform (one less than the number of (x, y) point pairs), d = planar extent (diameter) of the waveform, and L = total length of the waveform. Under this formulation, fractal dimensions range from D = 1.0, for straight lines through approximately D = 1.15 for random-walk waveforms, to D approaching 1.5 for the most convoluted waveforms. The fractal characterization may be especially useful for analyzing and comparing complex waveforms such as electroencephalograms (EEGs).

Mesh:

Year:  1988        PMID: 3396335     DOI: 10.1016/0010-4825(88)90041-8

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  69 in total

1.  Ventricular beat classifier using fractal number clustering.

Authors:  H Bakardjian
Journal:  Med Biol Eng Comput       Date:  1992-09       Impact factor: 2.602

2.  Effect of imipramine on linear and nonlinear measures of heart rate variability in children.

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Journal:  Pediatr Cardiol       Date:  2003-11-07       Impact factor: 1.655

3.  Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques.

Authors:  Sonia Charleston-Villalobos; Guadalupe Dorantes-Méndez; Ramón González-Camarena; Georgina Chi-Lem; José G Carrillo; Tomás Aljama-Corrales
Journal:  Med Biol Eng Comput       Date:  2010-07-21       Impact factor: 2.602

4.  EEG signal analysis: a survey.

Authors:  D Puthankattil Subha; Paul K Joseph; Rajendra Acharya U; Choo Min Lim
Journal:  J Med Syst       Date:  2010-04       Impact factor: 4.460

5.  Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis.

Authors:  S Hadjidimitriou; A Zacharakis; P Doulgeris; K Panoulas; L Hadjileontiadis; S Panas
Journal:  Med Biol Eng Comput       Date:  2010-04-20       Impact factor: 2.602

6.  Nonlinear dynamics of cardiovascular ageing.

Authors:  Y Shiogai; A Stefanovska; P V E McClintock
Journal:  Phys Rep       Date:  2010-03       Impact factor: 25.600

7.  Influence of age on linear and nonlinear measures of autonomic cardiovascular modulation.

Authors:  Michael K Boettger; Steffen Schulz; Sandy Berger; Manuel Tancer; Vikram K Yeragani; Andreas Voss; Karl-Jürgen Bär
Journal:  Ann Noninvasive Electrocardiol       Date:  2010-04       Impact factor: 1.468

8.  Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals.

Authors:  Zulfiqar Ali; Irraivan Elamvazuthi; Mansour Alsulaiman; Ghulam Muhammad
Journal:  J Med Syst       Date:  2015-11-03       Impact factor: 4.460

9.  An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge.

Authors:  A Singh; B S Saini; D Singh
Journal:  Med Biol Eng Comput       Date:  2015-08-08       Impact factor: 2.602

Review 10.  Heart rate variability: a review.

Authors:  U Rajendra Acharya; K Paul Joseph; N Kannathal; Choo Min Lim; Jasjit S Suri
Journal:  Med Biol Eng Comput       Date:  2006-11-17       Impact factor: 2.602

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