Noise Removal in Single-Lead Capacitive ECG With Adaptive Filtering and Singular Value Decomposition

Capacitive electrodes have been proposed to measure the electrocardiography (ECG) without direct skin contact. Unfortunately, capacitive ECG (cECG) measurements are very sensitive to various types of noise, and are therefore limited for heart-rate monitoring. The aim of the present study is to explore the feasibility of a cECG system with dedicated denoising algorithms for clinical diagnosis. We conducted real cECG measurements on 12 healthy subjects while simultaneously obtaining traditional wet ECG measurements as references. An adaptive canceller and an innovative singular value decomposition (SVD) algorithm were applied to single-lead cECG for noise reduction. The correlation coefficients (CCs) between the processed cECG and the wet ECG were computed with and without the QRS complex as performance metrics. In addition, several clinical parameters were annotated from the detailed waves of both ECG signals by a medical expert and then compared. The results demonstrate high CCs between the cECG and wet ECG for both conditions. Furthermore, the clinical parameters identified from both signals are quite similar. These findings indicate the potential of cECG for morphological analysis in clinical settings.