Author/Authors :
Ayatollahi,, A iran university of science and technology - Department of Electrical Engineering, تهران, ايران , Jafarnia Dabanloo,, N iran university of science and technology - Department of Electrical Engineering, تهران, ايران , McLernon,, DC University of Leeds, - School of Electronic and Electrical Engineering, UK. , Johari Majd,, V tarbiat modares university - Electrical Engineering Department, تهران, ايران , Zhang, H Leeds University, - Biology Department, UK
Abstract :
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.
Keywords :
ECG , neural networks , heart rate variability , dynamical systems.
