Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring
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 pop...
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| Format: | Article |
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MDPI AG
2017-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/6/4/84 |
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| author | Omar J. Escalona William D. Lynn Gilberto Perpiñan Louise McFrederick David J. McEneaney |
| author_facet | Omar J. Escalona William D. Lynn Gilberto Perpiñan Louise McFrederick David J. McEneaney |
| author_sort | Omar J. Escalona |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T12:51:11Z |
| format | Article |
| id | doaj.art-e20a0aa06da74952a58b3af6d5abfeb6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T12:51:11Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e20a0aa06da74952a58b3af6d5abfeb62022-12-22T04:23:13ZengMDPI AGElectronics2079-92922017-10-01648410.3390/electronics6040084electronics6040084Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm MonitoringOmar J. Escalona0William D. Lynn1Gilberto Perpiñan2Louise McFrederick3David J. McEneaney4School of Engineering, Ulster University, Newtownabbey BT37 0QB, UKElectronics Department, Northern Regional College, Coleraine BT52 1QA, UKElectronics and Circuits Department, Universidad Simon Bolivar, Caracas 89000, VenezuelaSchool of Engineering, Ulster University, Newtownabbey BT37 0QB, UKCraigavon Area Hospital—SHSCT, Craigavon BT63 5QQ, UKAbnormal 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).https://www.mdpi.com/2079-9292/6/4/84arm-ECGbipolar ECG leadlong-term ECGwearable ECG monitoringparoxysmal arrhythmiasEEMDEMDsignal averagingECG denoisingFFT |
| spellingShingle | Omar J. Escalona William D. Lynn Gilberto Perpiñan Louise McFrederick David J. McEneaney Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring Electronics arm-ECG bipolar ECG lead long-term ECG wearable ECG monitoring paroxysmal arrhythmias EEMD EMD signal averaging ECG denoising FFT |
| title | Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring |
| title_full | Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring |
| title_fullStr | Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring |
| title_full_unstemmed | Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring |
| title_short | Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring |
| title_sort | data driven ecg denoising techniques for characterising bipolar lead sets along the left arm in wearable long term heart rhythm monitoring |
| topic | arm-ECG bipolar ECG lead long-term ECG wearable ECG monitoring paroxysmal arrhythmias EEMD EMD signal averaging ECG denoising FFT |
| url | https://www.mdpi.com/2079-9292/6/4/84 |
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