Abstract
Motion artefact is the biggest concern in Electrocardiogram signals, especially when recording long-term measurements. Current studies fail to address the major source of motion artefact, which is skin stretch. This study utilizes two-dimensional strain fields as motion information in two advanced algorithms- Principal Component Analysis (PCA) and Independent Component Analysis (ICA). The strain fields were computed using point tracking and infinitesimal strain theory and a comparison of mean and median strains as motion information was made. The highest improvement in Signal to Noise ratio (SNR) was observed when the mean values of strain fields over a region of interest per ECG sample were taken as motion information in ICA. The lowest SNRs were obtained when PCA and ICA were implemented without any motion information.
Highlights
Electrocardiography (ECG) gives a measurement of the electrical activity of the heart over time
4.2 Improvements in Signal to Noise ratio (SNR) Using Mean and Median Strains A comparison between using mean and median values of strains as motion information in Principal Component Analysis (PCA) and Independent Component Analysis (ICA) was done to find the improvements in SNRs (Table 1)
SNR noisy and SNR after PCA/ICA were calculated using equations 3 and 4, where 'ref ECG' is the one lead ECG at rest, acquired using gel Ag/AgCl electrodes on shoulders. 'noisy ECG' represents ECG acquired during skin-stretch, and 'ECG PCA/ICA' represents filtered ECG after using motion information in PCA and ICA
Summary
Electrocardiography (ECG) gives a measurement of the electrical activity of the heart over time. It is an important clinical diagnostic measure that is widely known and practiced. In order to diagnose paroxysmal arrhythmias in ECG, it is important that it is measured over a long period of time (from several weeks to months). In some cases ECG is measured on a treadmill to diagnose arrhythmia- called stress testing. A major issue in long term measurements and stress testing is the introduction of motion artefacts which are difficult to filter out due to the same morphology and frequency as ECG. ECG with and without motion artefacts can be seen in figure 1
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