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 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).

[1]  Yu Zhou,et al.  An oversampling system for ECG acquisition , 2009 .

[2]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[3]  Li-Sheng Hsu,et al.  Bared Microtip Arrays for ECG Measurement , 2016 .

[4]  Wan Kyun Chung,et al.  Curved Microneedle Array-Based sEMG Electrode for Robust Long-Term Measurements and High Selectivity , 2015, Sensors.

[5]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[6]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[7]  T. Chien,et al.  Study of Single-Arm Electrode for ECG Measurement Using Flexible Print Circuit , 2010 .

[8]  Omar J. Escalona.  Analog implementation of the single fiducial point alignment technique for real-time high resolution ECG analysis in the P-R interval , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[9]  Omar J. Escalona,et al.  Performance assessment of dry electrodes for wearable long term cardiac rhythm monitoring: Skin-electrode impedance spectroscopy , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  David McEneaney,et al.  Arm and wrist surface potential mapping for wearable ECG rhythm recording devices: a pilot clinical study , 2013 .

[11]  O. J. Escalona,et al.  Fast and reliable QRS alignment technique for high-frequency analysis of signal-averaged ECG , 2006, Medical & Biological Engineering & Computing.

[12]  Omar J. Escalona,et al.  Electrocardiographic waveforms fitness check device technique for sudden cardiac death risk screening , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  P. Rajesh Kumar,et al.  Removal of noise from electrocardiogram using digital FIR and IIR filters with various methods , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[14]  B. Shackel,et al.  The assessment of chair comfort. , 1969, Ergonomics.

[15]  B. Lin,et al.  Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview , 2017 .

[16]  M. Josephson,et al.  Evaluation of patients with palpitations. , 1998, The New England journal of medicine.

[17]  Cesar Navarro,et al.  Real-time system for high-resolution ECG diagnosis based on 3D late potential fractal dimension estimation , 2011, 2011 Computing in Cardiology.

[18]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[19]  O. Escalona,et al.  Approaching Long Term Cardiac Rhythm Monitoring Using Advanced Arm Worn Sensors and ECG Recovery Techniques , 2015 .

[20]  Enzo Pasquale Scilingo,et al.  Recent Advances on Wearable Electronics and Embedded Computing Systems for Biomedical Applications , 2017 .

[21]  A. Camm,et al.  A Clinical Perspective on Sudden Cardiac Death. , 2015, Arrhythmia & electrophysiology review.

[22]  Norden E. Huang,et al.  The Multi-Dimensional Ensemble Empirical Mode Decomposition Method , 2009, Adv. Data Sci. Adapt. Anal..

[23]  Steffen Leonhardt,et al.  A Novel 12-Lead ECG T-Shirt with Active Electrodes , 2016 .

[24]  Norbert Noury,et al.  On designing an ubiquitous sensor network for health monitoring , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[25]  Wei Zhou,et al.  Fabrication of Micro-Needle Electrodes for Bio-Signal Recording by a Magnetization-Induced Self-Assembly Method , 2016, Sensors.

[27]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.