No 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).

A clinical database of distal electrogram recordings was created in conjunction with the Craigavon Area Hospital Cardiac Research Department. Signal averaged ECG (SAECG) methods were then used to inspect electrograms recorded bilaterally in a pilot study and the evidence based outcome of which directed the WASTCArD research group to consider the left arm as a prime location for a potential long term cardiac monitor. Empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and data fusion (DF) techniques were developed due to their ability to extract morphologically intact information from a dynamic data stream and their performance compared to the control SAECG reference method and clinically accepted denoising approach in high-resolution electrocardiography. EEMD was found to be a robust, low latency denoising technique, in comparison to SAECG performance; achieving signal to noise enhancement figures that, in some cases, improved on the SAECG control method, when used with far-field bipolar leads along the left arm ECG data.

Empirical mode decomposition is used as a low latency method of recovering the cardiac ventricular activity QRS biopotential signals recorded from the upper arm. The recovery technique is tested and compared with the industry accepted technique of signal averaging using a database of "normal" rhythm traces from bipolar ECG leads along the left arm, recorded from patient volunteers at a cardiology day procedure clinic. The same partial recomposition technique is applied to recordings taken using an innovative dry electrode technology supplied by Plessey Semiconductors. In each case, signal to noise ratio (SNR) is used as a metric for comparison.

According to recent British Heart Foundation statistics, one in six men and more than one in ten women die from coronary heart disease (CHD) in the UK. This equates to almost 74,000 deaths per annum from CHD alone. More worryingly, every week, 12 apparently fit and healthy young people aged 35 and under, die from undiagnosed cardiac conditions. In both circumstances, monitoring is preformed only when triggered by an event. Unfortunately, this may be too late in the large majority of cases. For instance, there is evidence suggesting that most indiscernible cardiac abnormalities are made detectable by ECG through the act of suddenly standing upright. This infers that the condition would be detectable during the course of everyday ambulatory activity and highlights the need for a long term monitoring device. Current diagnostic equipment consists of the Holter monitor