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

Escalona, OJ, Lynn, WD, Perpiñan, GI, McFrederick, L and McEneaney, DJ (2017) Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring. Electronics, 6 (84). pp. 1-18. [Journal article]

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URL: http://www.mdpi.com/journal/electronics

DOI: 10.3390/electronics6040084


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 theambulatory population, atrial fibrillation is the most common arrhythmia and is associated with anincreased 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 earlystages of disease arrhythmias may be transient, lasting only a few seconds, and are thus difficultto 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).

Item Type:Journal article
Keywords:Arm-ECG; bipolar ECG lead; long-term ECG; wearable ECG monitoring; paroxysmal arrhythmias; EEMD; EMD; signal averaging; ECG denoising; FFT
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
ID Code:39009
Deposited By: Professor Omar Escalona
Deposited On:08 Jan 2018 11:18
Last Modified:08 Jan 2018 11:18

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