@inproceedings{7a34d0dcb0d14ae3b698d2153decac82,
title = "Arm-ECG Bipolar Leads Signal Recovery Methods for Wearable Long-term Heart Rate and Rhythm Monitoring",
abstract = "A clinical database of distal electrogram recordings was created in conjunction with the Craigavon Area Hospital Cardiac Research Department. Signal averaged ECG (SAECG) methods were then used to inspect electrograms recorded bilaterally in a pilot study and the evidence based outcome of which directed the WASTCArD research group to consider the left arm as a prime location for a potential long term cardiac monitor. Empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and data fusion (DF) techniques were developed due to their ability to extract morphologically intact information from a dynamic data stream and their performance compared to the control SAECG reference method and clinically accepted denoising approach in high-resolution electrocardiography. EEMD was found to be a robust, low latency denoisingtechnique, in comparison to SAECG performance; achieving signal to noise enhancement figures that, in some cases, improved on the SAECG control method, when used with far-field bipolar leads along the left arm ECG data.",
keywords = "Bipolar ECG leads, long term monitoring, BIS sensors, AgCl dry electrodes, Signal-to-Noise ratio, SNR, far-field EGM, bi-polar leads, denoising, signal averaging, SFP alignment technique.",
author = "WD Lynn and OJ Escalona and P Vizcaya and DJ McEneaney",
year = "2017",
month = oct,
day = "11",
language = "English",
volume = "44",
editor = "Alan Murray",
booktitle = "Unknown Host Publication",
publisher = "Computing in Cardiology",
note = "44th Computing in Cardiology Conference, 2017, Rennes, France ; Conference date: 11-10-2017",
}