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This study evaluates the performance of three data-driven denoising approaches for enhancing the quality of cardiac electrograms recorded from bipolar leads on the left arm. An arm surface potential mapping clinical dataset was developed and used retrospectively for this study. The study arm-ECG dataset consisted of 58 cases of left-arm ECG bipolar leads gathered at the Craigavon Area Hospital, in Northern Ireland. The denoising methods considered were the Daubechies, order 10 (Db10), discrete wavelet transform (DWT), the discrete Meyer (Dmey) wavelet transform, and Empirical Mode Decomposition (EMD). Performance evaluation is quantified by measurements of the signal-to-noise ratio (SNR). The performance results on two bipolar left-arm ECG leads: Lead-1 (on the upper-arm, transversally) and Lead-4 (axially, along the upper-arm), yielded (Mean [min-max]) values for Dmey, Db10, EMD and SA of 11.8 [0.9-49.2], 13.9 [ 0,97-79.4], 17.9 [0.7-55.8] and 98.9 [1.15-649.6], respectively, for Lead-1. On Lead-4, the SNR performance results were: Dmey (6.12 [0.89-22.2]), Db10 (6.29 [0.84-19.6]), EMD (11.2 [2.19-99.2]) and SA (45.1 [4.9-200.4]). Therefore, the study revealed that the EMD based denoising technique offered the best performance.

2019 computing in cardiology (cinc)

Denoising Performance of Discrete Wavelet Transform and Empirical Mode Decomposition Based Techniques on Monitoring Cardiac Electrograms From the Left-Arm