A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells.

Models of cardiac cell electrophysiology are complex non-linear systems which can be used to gain insight into mechanisms of cardiac dynamics in both healthy and pathological conditions. However, the complexity of cardiac models can make mechanistic insight difficult. Moreover, these are typically fitted to averaged experimental data which do not incorporate the variability in observations.

Simple T-Wave Metrics May Better Predict Early Ischemia as Compared to ST Segment.

There is pressing clinical need to identify developing heart attack (infarction) in patients as early as possible. However, current state-of-the-art tools in clinical practice, underpinned by the evaluation of elevation of the ST segment of the 12-lead electrocardiogram (ECG), do not identify all patients suffering from lack of blood flow to the heart muscle (cardiac ischemia), worsening the risk for further adverse events and patient outcome overall. In this study, we aimed to explore and compare the portions of cardiac repolarization in the ECG that best capture the electrophysiological changes associated with ischemia.

The development of a new computational model for the electromechanics of the human ventricular myocyte.

In this work we present a new electromechanical cardiac myocyte model tailored to reproduce the electrical and force generating activities of human ventricular myocytes. The model was created by coupling two existing models: the ten Tusscher electrophysiology model and the Rice myofilament mechanics model. The parameters of the new model were adjusted in order to replicate the available experimental data for human myocytes.