Analysing effects of implant dimensions on electrocardiograph: A modeling approach

Abstract

IntroductionThe research and development of wireless and/or implantable measurement devices for ECG measurements has been continuously increasing during the 21st century [1][2][3]. These modern measurement devices offer stable and long-term monitoring possibilities. The implantable ECG monitors have been noticed to be efficient and useful in detecting various cardiac arrhythmias and ECG patterns and thus their use is continuously increasing [4][5][6][7].Implantation of measurement device into the human body is time-consuming and expensive. The effects of different implant designs on the measurement cannot thus be tested and reviewed by actually implanting the device into humans during the design process. There is thus a need for methods providing information about the effects without actual implantation of a device in test subjects. Modeling affords an effective means of investigating the effects of implant characteristics such as implant dimensions on the ECG signal. This information would be available without expensive and time-consuming in vivo trials. Most of the modeling studies related to implantable cardiac devices have concentrated on modeling the current distributions generated by stimulation devices such as implantable cardiac defibrillators [8][9][10][11][12][13] but the effects of implantation and implant dimensions on the sensitivity distributions have been also studied [17].In the present study computational modeling methods were applied to provide information on the effects of implantable ECG monitor designs on measured ECG. The methods applied were lead field and finite difference modeling approaches together with state-machine based cardiac activation model. The lead field and reciprocity approach [14] with the finite difference method (FDM) [15,16] has been efficiently applied in studies where body surface measurements have been analyzed. The objectives of the study were to analyze the effects of implant dimensions on the measured ECG by applying simulated activation sequence together with lead fields and evaluate how the results differ from the previously published results [17] when only properties of lead fields were analyzed. Methods Model dataThe present study applies the finite difference method (FDM) in modeling the electric fields in the volume conductor. In the FDM the segmented volume data, e.g. from an MRI dataset, are divided into cubic elements forming a resistive network. The FDM is based on the Poisson's equation which describes the bioelectric quasistatic source-field problems. Poisson's equations use the information of the conductivities and shape of the volume conductor acquired from the image data. Continuous Poisson's functions are discretized to form linear equations which describe the model. A potential distribution within the model for a specific source configuration is solved with these linear equations with iterative methods. The resulting potential distribution is applied when sensitivity distribution, i.e. lead field, is solved. [18] The FDM allows the impleme...

Modelling the heart as a communication system

“…The model equations were solved using a finite difference method for spatial derivatives and explicit Euler integration for time derivatives assuming Neumann boundary conditions. Cellular automata (CA) models have been used to study cardiac action potential propagation in several previous studies [31][32][33][34][35][36][37][38][39][40]. The model we used employed realistic restitution properties and the curvature phenomenon [41].…”