Fractal behavior of the electrocardiogram: distinguishing heart-failure and normal patients using wavelet analysis

Author

Teich, Malvin C.

Author_Institution

Dept. of Biomed. Eng., Boston Univ., MA, USA

Volume

3

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1031

Abstract

Heart-failure patients can be identified by computing certain statistics of the sequence of heartbeats, such as the Allan factor (AF) and its generalization, the wavelet Allan factor (WAF). These measures successfully determine whether a given patient suffers from heart failure or not. They succeed because they are jointly sensitive to both short-term, and long-term fractal, characteristics of the heartbeat time series

Keywords

electrocardiography; fractals; medical signal processing; time series; wavelet transforms; ECG fractal behavior; electrodiagnostics; heart-failure patients; heartbeat time series; heartbeats sequence statistics; long-term fractal characteristics; normal patients; short-term fractal characteristics; wavelet Allan factor; wavelet analysis; Biology computing; Biomedical computing; Biomedical measurements; Engineering in Medicine and Biology Society; Fractals; Heart; Signal processing; Time frequency analysis; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.652695

Filename

652695