A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution.
Robin MossEike M WülfersRaphaela LewetagTibor HornyikStefanie Perez-FelizTim StrohbachMarius MenzaAxel KrafftKatja E OdeningGunnar SeemannPublished in: PloS one (2022)
Computational modeling of electrophysiological properties of the rabbit heart is a commonly used way to enhance and/or complement findings from classic lab work on single cell or tissue levels. Yet, thus far, there was no possibility to extend the scope to include the resulting body surface potentials as a way of validation or to investigate the effect of certain pathologies. Based on CT imaging, we developed the first openly available computational geometrical model not only of the whole heart but also the complete torso of the rabbit. Additionally, we fabricated a 32-lead ECG-vest to record body surface potential signals of the aforementioned rabbit. Based on the developed geometrical model and the measured signals, we then optimized the activation sequence of the ventricles, recreating the functionality of the Purkinje network, and we investigated different apico-basal and transmural gradients in action potential duration. Optimization of the activation sequence resulted in an average root mean square error between measured and simulated signal of 0.074 mV/ms for all leads. The best-fit T-Wave, compared to measured data (0.038 mV/ms), resulted from incorporating an action potential duration gradient from base to apex with a respective shortening of 20 ms and a transmural gradient with a shortening of 15 ms from endocardium to epicardium. By making our model and measured data openly available, we hope to give other researchers the opportunity to verify their research, as well as to create the possibility to investigate the impact of electrophysiological alterations on body surface signals for translational research.
Keyphrases
- mass spectrometry
- multiple sclerosis
- ms ms
- heart failure
- single cell
- heart rate variability
- computed tomography
- electronic health record
- magnetic resonance imaging
- big data
- left ventricular
- high resolution
- blood pressure
- machine learning
- amino acid
- image quality
- high speed
- artificial intelligence
- contrast enhanced
- fluorescence imaging
- single molecule