Stationary wavelet transform and principal component analysis application on capacitive electrocardiography

The gradual aging and the prevalence of cardiac pathologies in society has motivated an increase in research of homecare technologies. Capacitive sensing of the electrocardiogram (ECG) allows unobtrusively monitoring of the most well-known physiological signal. However, this measurement process is relentlessly superimposed with interferences and noise, namely movement artifacts which nearly erase the signal. This work presents a system implementing a new data processing technique, based on stationary wavelet transform together with principal component analysis. This approach is successful, as it is able to characterize motion artifacts, and to improve the quality of the ECG acquired without the subject's knowledge.