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Electronics 2017, 6(4), 84; https://doi.org/10.3390/electronics6040084

Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

1
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
2
Electronics Department, Northern Regional College, Coleraine BT52 1QA, UK
3
Electronics and Circuits Department, Universidad Simon Bolivar, Caracas 89000, Venezuela
4
Craigavon Area Hospital—SHSCT, Craigavon BT63 5QQ, UK
*
Author to whom correspondence should be addressed.
Received: 1 September 2017 / Revised: 1 October 2017 / Accepted: 11 October 2017 / Published: 15 October 2017
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Abstract

Abnormal heart rhythms (arrhythmias) are a major cause of cardiovascular disease and death in Europe. Sudden cardiac death accounts for 50% of cardiac mortality in developed countries; ventricular tachycardia or ventricular fibrillation is the most common underlying arrhythmia. In the ambulatory population, atrial fibrillation is the most common arrhythmia and is associated with an increased risk of stroke and heart failure, particularly in an aging population. Early detection of arrhythmias allows appropriate intervention, reducing disability and death. However, in the early stages of disease arrhythmias may be transient, lasting only a few seconds, and are thus difficult to detect. This work addresses the problem of extracting the far-field heart electrogram signal from noise components, as recorded in bipolar leads along the left arm, using a data driven ECG (electrocardiogram) denoising algorithm based on ensemble empirical mode decomposition (EEMD) methods to enable continuous non-invasive monitoring of heart rhythm for long periods of time using a wrist or arm wearable device with advanced biopotential sensors. Performance assessment against a control denoising method of signal averaging (SA) was implemented in a pilot study with 34 clinical cases. EEMD was found to be a reliable, low latency, data-driven denoising technique with respect to the control SA method, achieving signal-to-noise ratio (SNR) enhancement to a standard closer to the SA control method, particularly on the upper arm-ECG bipolar leads. Furthermore, the SNR performance of the EEMD was improved when assisted with an FFT (fast Fourier transform ) thresholding algorithm (EEMD-fft). View Full-Text
Keywords: arm-ECG; bipolar ECG lead; long-term ECG; wearable ECG monitoring; paroxysmal arrhythmias; EEMD; EMD; signal averaging; ECG denoising; FFT arm-ECG; bipolar ECG lead; long-term ECG; wearable ECG monitoring; paroxysmal arrhythmias; EEMD; EMD; signal averaging; ECG denoising; FFT
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Escalona, O.J.; Lynn, W.D.; Perpiñan, G.; McFrederick, L.; McEneaney, D.J. Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring. Electronics 2017, 6, 84.

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