Key result
The unscented Kalman filter produced more accurate estimates when process noise was low compared to measurement noise, whereas the gain-scheduled Kalman filter was more accurate for higher process noise.
Why the study?
Existing measurement techniques cannot simultaneously monitor all cardiac electrophysiological quantities contributing to arrhythmia, and few comparisons exist of different state estimation algorithms applied to the same action potential model.
In a computational model of cardiac action potentials, the unscented Kalman filter outperformed the gain-scheduled Kalman filter when process noise was low relative to measurement noise, and estimating the slow variable from the fast variable yielded lower errors than the reverse.
Answers are grounded in this paper and cite their sources.
Noise-dependent filter choice may optimize cardiac model estimation; leaves open clinical translation without in vivo validation.
Various technologies, such as electrocardiography, optical mapping, and patch clamping, have been developed to monitor cardiac electrophysiological behavior in live tissue. One limitation is that none of the available measurement methods is capable of monitoring simultaneously all quantities, such as intracellular ionic concentrations and ion-channel gating states, that may be important contributors to arrhythmia formation. Data assimilation strategies such as Kalman filtering can be used to fill in missing measurements, but to our knowledge, there have been few comparisons of different state estimation algorithms applied to the same cardiac action potential model.
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Muñoz et al. (2020) studied Cardiac action potential dynamics. Unscented Kalman filter (UKF) vs. Gain-scheduled Kalman filter (GSKF) was evaluated on Mean absolute relative estimation errors. The unscented Kalman filter produced more accurate estimates when process noise was low compared to measurement noise, whereas the gain-scheduled Kalman filter was more accurate for higher process noise.
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