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

OJ Escalona, WD Lynn, GI Perpiñan, L McFrederick, DJ McEneaney

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 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).
LanguageEnglish
Pages1-18
JournalElectronics
Volume6
Issue number84
DOIs
Publication statusPublished - 15 Oct 2017

Fingerprint

Cardiac Arrhythmias
Electrocardiography
Arm
Signal-To-Noise Ratio
Sudden Cardiac Death
Ventricular Fibrillation
Fourier Analysis
Ventricular Tachycardia
Wrist
Developed Countries
Atrial Fibrillation
Population
Cardiovascular Diseases
Heart Failure
Stroke
Lead
Equipment and Supplies
Mortality

Keywords

  • Arm-ECG
  • bipolar ECG lead
  • long-term ECG
  • wearable ECG monitoring
  • paroxysmal arrhythmias
  • EEMD
  • EMD
  • signal averaging
  • ECG denoising
  • FFT

Cite this

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title = "Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring",
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 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).",
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month = "10",
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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; McEneaney, DJ.

In: Electronics, Vol. 6, No. 84, 15.10.2017, p. 1-18.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

AU - Escalona, OJ

AU - Lynn, WD

AU - Perpiñan, GI

AU - McFrederick, L

AU - McEneaney, DJ

PY - 2017/10/15

Y1 - 2017/10/15

N2 - 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).

AB - 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).

KW - Arm-ECG

KW - bipolar ECG lead

KW - long-term ECG

KW - wearable ECG monitoring

KW - paroxysmal arrhythmias

KW - EEMD

KW - EMD

KW - signal averaging

KW - ECG denoising

KW - FFT

UR - http://www.mdpi.com/journal/electronics

U2 - 10.3390/electronics6040084

DO - 10.3390/electronics6040084

M3 - Article

VL - 6

SP - 1

EP - 18

JO - Electronics

T2 - Electronics

JF - Electronics

SN - 2079-9292

IS - 84

ER -