Key result
Principal and independent component analysis of noisy ECG signals can extract the noise signal for use in adaptive noise cancellation to improve signal quality.
Why the study?
Adaptive filtering effectively cancels noise in ECG signals but typically requires a reference signal or estimation of the noise signal.
PCA and ICA can be used to extract noise signals for adaptive noise cancellation in ECG signals, potentially improving signal quality without requiring a separate reference signal.
Proposed ECG reference signal method aids denoising research; leaves open clinical adoption without validation.
In this paper, the proposed methodology has suggested a way to fulfil the need of reference signal for adaptive filtering (AF) of electrocardiogram (ECG) signals. ECG signals are the most important form of representation and observation of different heart conditions. During recording process the ECG signals gets contaminated with different types of noises like, baseline wander (BW), electrode motion artefact (MA), and muscle noise also known as electromyogram (EMG). Noise contamination causes distortion of normal structure of ECG signal. Adaptive filters works fine for ECG noise cancellation. But, the problem is the need of reference signal or estimation of noise signal. To solve this problem principal and independent component (PCA and ICA) of noisy signal has been analysed to extract the noise signal, which is used in adaptive noise cancellation of ECG signals. Fidelity parameters like mean square error (MSE), signal to noise ratio (SNR) and maximum error (ME) has been observed to measure the quality of filtered signals.
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Ahirwal et al. (2022) studied ECG noise contamination. Principal and independent component-based analysis (PCA and ICA) was evaluated on Fidelity parameters (MSE, SNR, ME). Principal and independent component analysis of noisy ECG signals can extract the noise signal for use in adaptive noise cancellation to improve signal quality.
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