A comprehensive model using modified Zeeman model for generating ECG signals
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran University of Science and Technology
2005-04-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/browse.php?a_code=A-10-3-47&slc_lang=en&sid=1 |
| Summary: | 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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| ISSN: | 1735-2827 2383-3890 |