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

Omar Escalona,William Lynn,Louise Mcfrederick,Gilberto Perpiñan,David Mceneaney

doi:10.3390/electronics6040084

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).

Highlights

Cardiovascular disease remains globally the commonest cause of death both in developing and developed nations
Lies in extracting an accurate electrocardiogram from selected bipolar arm-ECG leads from pairs of leads from pairs of electrodes placed remotely from the heart. To this end we have electrodes placed remotely from the heart. To this end we have proposed to employ proposed to employ advanced signal processing and data‐driven denoising techniques to extract the advanced signal processing and data-driven denoising techniques to extract the ventricular activity ventricular activity (QRS complex, ST segment and T wave), as well as the atrial activity
After pre‐filtering (50 Hz notch and 0.2–40 Hz band‐pass) the set of seven left‐arm bipolar leads process) as a control ECG for the performance assessment, for each of the 34 clinical cases (N = 34), and the recorded standard chest Lead I

Summary

Introduction

Cardiovascular disease remains globally the commonest cause of death both in developing and developed nations. Usually caused by lethal arrhythmias, usually ventricular tachycardia or fibrillation, account for 50% of these deaths [1]. Mortality from sudden death increases by 10% per minute. 10% survive to be discharged from hospital. In those cases where defibrillation occurs within 5–7 min survival increases to around 30%. A wrist-based monitor to detect ventricular arrhythmia with the activation of a layperson or first responder network to deliver prompt defibrillation would permit early defibrillation and improve survival from out-of-hospital cardiac arrest

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