A brief description of predominant noises in ECG is given below. These noises and artefacts lie within the spectral range of interest and manifest themselves pre-dominantly as morphological features similar to the inherent aspects of the ECG or similar to any disease-specific aspects. The denoiser converts into Lesser the difference between and better is the performance of the denoising model. The denoiser aims at estimating the signal with the help of a denoising method (Fig. (1) where is some noise process with variance σ 2. These applications require a proper determination of the morphological and interval aspects of the recorded ECG signal, which are susceptible to various kinds of predominant noises such as base-line wander (BW), muscle artefacts (MA) or electromyogram (EMG) noise, channel noise (additive white Gaussian noise, AWGN), power-line interference (PLI), and miscellaneous noises such as composite noise (CN), random noise, electrode motion artefacts (EM), and instrumentation noise, making it challenging to determine disease-specific morphological anomalies in the ECG signals. ECG signals have a wide variety of applications in the medical domain such as cardiorespiratory monitoring, seizure detection and monitoring, ECG-based biometrics authentication, real-time analysis of electrocardiographic rhythm, heart-rate variability analysis using smart electrocardiography patch, and study of cardiac ischemia. 1 a shows various components of ECG signal which are of two types, viz., morphological features: P-wave, QRS-complex, T-wave, and U-wave and interval features: PR-segment, ST-segment, PR interval, ST interval, RR interval, and so on. ECG is recorded by measuring the potential difference between two electrodes placed on the patient's skin.įig. However, ECG is susceptible to different types of noises, which might distort the morphological features and the interval aspects of the ECG leading to a false diagnosis and improper treatment of patients. ECG monitoring and subsequent analyses find a lot of applications in the medical domain. The information contained within ECG is both physiological and pathological, which are integral to the diagnosis of heart diseases. It gives information about heart rate, rhythm, and electrical activity. It is a time-varying bio-signal reflecting the ionic current flow, which causes contractions and subsequent relaxations in the cardiac fibres and provide indirect insight into the blood flow to the heart muscle. It is a wide-spread tool to examine the electrical and muscular functions of the heart. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.Įlectrocardiogram (ECG) is a non-linear non-stationary quasi-periodic time series. For power-line interference removal, DLSR and EWT perform well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. IET Generation, Transmission & DistributionĪn electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
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