A Pipeline for Adaptive Filtering and Transformation of Noisy Left-Arm ECG to Its Surrogate Chest Signal
| Autor: | Farzad Mohaddes, Rafael da Silva, Fatma Akbulut, Yilu Zhou, Akhilesh Tanneeru, Edgar Lobaton, Bongmook Lee, Veena Misra |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
020205 medical informatics
Mean squared error Computer Networks and Communications Computer science left-arm ECG wearable ECE armband single-lead ECG lcsh:TK7800-8360 02 engineering and technology Least mean squares filter adaptive filtering 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Recursive least squares filter ECG transformation business.industry filter optimization lcsh:Electronics 020207 software engineering Pattern recognition Filter (signal processing) Adaptive filter Kernel (image processing) Hardware and Architecture Control and Systems Engineering Signal Processing Artificial intelligence business |
| Zdroj: | Electronics, Vol 9, Iss 866, p 866 (2020) Electronics Volume 9 Issue 5 |
| ISSN: | 2079-9292 |
| Popis: | 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&minus 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. |
| Databáze: | OpenAIRE |
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