DeepAI AI Chat
Log In Sign Up

Deep Network for Capacitive ECG Denoising

by   Vignesh Ravichandran, et al.
Indian Institute Of Technology, Madras

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.


RespNet: A deep learning model for extraction of respiration from photoplethysmogram

Respiratory ailments afflict a wide range of people and manifests itself...

PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram

Photoplethysmogram (PPG) is increasingly used to provide monitoring of t...

Blind ECG Restoration by Operational Cycle-GANs

Continuous long-term monitoring of electrocardiography (ECG) signals is ...

Estimating Cardiac Tissue Conductivity from Electrograms with Fully Convolutional Networks

Atrial Fibrillation (AF) is characterized by disorganised electrical act...

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

Inter-beat interval (IBI) measurement enables estimation of heart-rate v...

Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring

Photoplethysmography (PPG) devices are widely used for monitoring cardio...

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

The high prevalence of cardiovascular diseases (CVDs) calls for accessib...