Title of article :
A modified Zeeman model for producing HRV signals and its application to ECG signal generation
Author/Authors :
Jafarnia-Dabanloo، نويسنده , , N. and McLernon، نويسنده , , D.C. and Zhang، نويسنده , , H. and Ayatollahi، نويسنده , , A. and Johari-Majd، نويسنده , , V.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2007
Pages :
10
From page :
180
To page :
189
Abstract :
Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)—all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the modelʹs ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.
Keywords :
Heart Rate Variability , dynamical systems , NEURAL NETWORKS , ECG
Journal title :
Journal of Theoretical Biology
Serial Year :
2007
Journal title :
Journal of Theoretical Biology
Record number :
1538220
Link To Document :
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