Volume 1, Issue 2 (April 2005)                   IJEEE 2005, 1(2): 88-93 | Back to browse issues page

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Abstract:   (12305 Views)
Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of its uses is for the assessment of diagnostic ECG signal processing devices. So the model should have the capability of producing a wide range of ECG signals, with all the nuances that reflect the sickness to which humans are prone, and this would necessarily include variations in heart rate variability (HRV). 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 the new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced using a radial basis function neural network.
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Type of Study: Research Paper |
Received: 2008/10/13 | Revised: 2011/07/09 | Accepted: 2011/07/09