Abstract
The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.
Highlights
U.S national health expenditure accounts for 17.9% of the GDP with a projected annual growth of5.5% per year between 2018 and 2027, reaching $6.0 trillion by 2027 [1,2,3]
It was observed that the signal-to-noise ratio (SNR) of the transformed signals that were calculated by Equation (16) improved by up to 144% and 155% for left-arm ECG (LA-ECG) and C-ECG, respectively
The accuracy of the LA-ECG and C-ECG signals obtained from a multi-metric armband system was validated against a BIOPAC benchtop system
Summary
U.S national health expenditure accounts for 17.9% of the GDP with a projected annual growth of. Due to the close vicinity to the heart, the chest is the most conventional location for ECG evaluations, enabling ECG mapping with high precision and signal-to-noise ratio (SNR) [11]. Located in the proximity to the heart, left arm is a good candidate for ECG measurements, providing further comfort for continual cardiac monitoring compared to chest-wearable devices. In this study and building on our prior art [13,18], we developed a Bluetooth Low Energy (BLE)-enabled ECG armband system capable of obtaining ECG signals from both the chest and left arm with high accuracy required for wearable devices. We modified the graphical user interface (GUI) proposed by Mugdha et al [19] to filter the sedentary chest and left-arm ECG signals that were contaminated by any of the five noises mentioned earlier. C-ECG replicas from noisy LA-ECG signals in non-sedentary and multi-subject scenarios, the diagnosis of various cardiac conditions such as atrial fibrillation of pathologic Q waves from LA-ECG signals may become possible [20,21,22,23,24,25]
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