Title :
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
fDate :
31 Oct-3 Nov 1996
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;
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
DOI :
10.1109/IEMBS.1996.652695
