  Continuous monitoring of cardiac health under free living condition is
crucial to provide effective care for patients undergoing post operative
recovery and individuals with high cardiac risk like the elderly. Capacitive
Electrocardiogram (cECG) is one such technology which allows comfortable and
long term monitoring through its ability to measure biopotential in conditions
without having skin contact. cECG monitoring can be done using many household
objects like chairs, beds and even car seats allowing for seamless monitoring
of individuals. This method is unfortunately highly susceptible to motion
artifacts which greatly limits its usage in clinical practice. The current use
of cECG systems has been limited to performing rhythmic analysis. In this paper
we propose a novel end-to-end deep learning architecture to perform the task of
denoising capacitive ECG. The proposed network is trained using motion
corrupted three channel cECG and a reference LEAD I ECG collected on
individuals while driving a car. Further, we also propose a novel joint loss
function to apply loss on both signal and frequency domain. We conduct
extensive rhythmic analysis on the model predictions and the ground truth. We
further evaluate the signal denoising using Mean Square Error(MSE) and Cross
Correlation between model predictions and ground truth. We report MSE of 0.167
and Cross Correlation of 0.476. The reported results highlight the feasibility
of performing morphological analysis using the filtered cECG. The proposed
approach can allow for continuous and comprehensive monitoring of the
individuals in free living conditions.
