Denoising and Artifacts Removal in ECG Signals

ECG signal is a non-stationary biological signal and plays a pivotal role in the diagnosis of cardiac-related abnormalities. Reduction of noise in electrocardiography signals is a crucial and important problem because the artifacts corrupting the signal possesses similar frequency characteristics as that of the signal itself. Conventional techniques viz. filtering were proved to be uncap able of eliminating these interferences. Therefore the electrocardiography signals require a novel and efficient denoising strategy with a view to facilitate satisfactory noise-removal performance. A new yet adaptive and data-driven method for denoising of ECG signals using EMD and DFA algorithms has been investigated...The proposed algorithm has been tested with ECG signals (MIT-BIH Database) with added noise such as baseline wander and muscle contraction noise. Parameter are calculated to determine the effectiveness of the algorithm on a variety of signal types. The obtained results show that the proposed denoising algorithm is easy to implement and suitable to be applied with electrocardiography signals

A robust ECG denoising technique using variable frequency complex demodulation

Background and Objective Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation

Адаптивное фильтрование дрейфа базовой линии нестационарных и нелинейных сигналов на основе метода эмпирического разложения

В статье рассматривается возможность применения эмпирической модовой декомпозиции (Empirical Mode Decomposition, EMD) для устранения дрейфа базовой линии на примере биомедицинских сигналов – измеряемых в клинике сигналов внутричерепного давления (ВЧД) и электрокардиограммы (ЭКГ). Для устранения нестационарной помехи из нестационарных и нелинейных сигналов используется адаптивное фильтрование на основе градиентного LMS-алгоритма Уидроу-Хоффа (Widrow-Hoff), в котором неизвест- ный опорный сигнал (вход в адаптивный фильтр) предлагается формировать с помощью внутренних модовых функций (IMF) эмпирического разложения исследуемого сигнала. Предлагаемая схема фильтрования, по сравнению с широко используемыми методами двухшаговой скользяще средней фильтрации, фильтром нижних частот нулевой фазы первого порядка и медианным фильтром, показала эффективное удаление дрейфа базовых линий ВЧД и ЭКГ сигналов без искажения их формы линий.У статті розглядається можливість застосування емпіричної модової декомпозиції (Empirical Mode Decomposition, EMD) для усунення дрейфу базової лінії на прикладі біомедичних сигналів – вимірюваних у клініці сигналів внутрішньочерепного тиску (ВЧТ) і електрокардіограми (ЕКГ). Для усунення нестаціонарної завади з нестаціонарних і нелінійних сигналів використовується адаптивне фільтрування на основі градієнтного LMS-алгоритму Уїдроу-Хоффа (Widrow-Hoff), у якому невідомий опорний сигнал (вхід в адаптивний фільтр) пропонується формувати за допомогою внутрішніх модових функцій (IMF) емпіричного розкладання досліджуваного сигналу. Запропонована схема фільтрування, у порівнянні з широко використовуваними методами двокрокової ковзне середньої фільтрації, фільтром нижніх частот нульової фази першого порядку і медіанним фільтром, показала ефективне усунення дрейфу базових ліній ВЧТ і ЕКГ сигналів без спотворення їх форми ліній.The goal of that work is check of the effectiveness of the presented EMD-method and the Widrow-Hoff gradient LMS-method for the baseline wander removal at ICP and electrocardiogram (ECG) signals, and comparison of the suggested method with statistically direct algorithms. The removal of such interference is a very important step in the preprocessing stage of essential medical signals for getting desired signal for clinical diagnoses. At this article a new method signal filtering was presented, in which the reconstruction of the reference signal is conditioned by lower frequency IMFs. This method does not use any preprocessing and post processing, and does not require prior estimates. The proposed filtering scheme, as compared to the widely used of a two-stage moving-average filter, lowpass-IIR and median filters, showed the effective baseline wander removal of ICP and EKG of signals without distortion of their waveform signals

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis

The Hilbert transform (HT) and associated Gabor analytic signal (GAS) representation are well-known and widely used mathematical formulations for modeling and analysis of signals in various applications. In this study, like the HT, to obtain quadrature component of a signal, we propose the novel discrete Fourier cosine quadrature transforms (FCQTs) and discrete Fourier sine quadrature transforms (FSQTs), designated as Fourier quadrature transforms (FQTs). Using these FQTs, we propose sixteen Fourier-Singh analytic signal (FSAS) representations with following properties: (1) real part of eight FSAS representations is the original signal and imaginary part is the FCQT of the real part, (2) imaginary part of eight FSAS representations is the original signal and real part is the FSQT of the real part, (3) like the GAS, Fourier spectrum of the all FSAS representations has only positive frequencies, however unlike the GAS, the real and imaginary parts of the proposed FSAS representations are not orthogonal to each other. The Fourier decomposition method (FDM) is an adaptive data analysis approach to decompose a signal into a set of small number of Fourier intrinsic band functions which are AM-FM components. This study also proposes a new formulation of the FDM using the discrete cosine transform (DCT) with the GAS and FSAS representations, and demonstrate its efficacy for improved time-frequency-energy representation and analysis of nonlinear and non-stationary time series.Comment: 22 pages, 13 figure

ECG signal denoising using a novel approach of adaptive filters for real-time processing

Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper

Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF)algorithms. However, the T waveform distortions introduced by the WTand the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WTto overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinicalBW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. /e results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG

An approach to diagnose cardiac conditions from electrocardiogram signals.

Lu, Yan."October 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 65-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Electrocardiogram --- p.1Chapter 1.1.1 --- ECG Measurement --- p.2Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6Chapter 1.2 --- Cardiovascular Disease --- p.7Chapter 1.3 --- Motivation --- p.9Chapter 1.4 --- Related Work --- p.10Chapter 1.5 --- Overview of Proposed Approach --- p.11Chapter 1.6 --- Thesis Outline --- p.13Chapter 2. --- ECG Signal Preprocessing --- p.14Chapter 2.1 --- ECG Model and Its Generalization --- p.14Chapter 2.1.1 --- ECG Dynamic Model --- p.14Chapter 2.1.2 --- Generalization of ECG Model --- p.15Chapter 2.2 --- Empirical Mode Decomposition --- p.17Chapter 2.3 --- Baseline Wander Removal --- p.20Chapter 2.3.1 --- Sources of Baseline Wander --- p.20Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21Chapter 2.4 --- ECG Denoising --- p.24Chapter 2.4.1 --- Introduction --- p.24Chapter 2.4.2 --- Instantaneous Frequency --- p.26Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28Chapter 2.4.4 --- Model-based Pre-filtering --- p.30Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35Chapter 2.4.7 --- Experiments --- p.39Chapter 2.5 --- Chapter Summary --- p.44Chapter 3. --- ECG Classification --- p.45Chapter 3.1 --- Database --- p.45Chapter 3.2 --- Feature Extraction --- p.46Chapter 3.2.1 --- Feature Selection --- p.46Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48Chapter 3.3 --- Classification by Support Vector Machine --- p.50Chapter 3.4 --- Experiments --- p.53Chapter 3.4.1 --- Performance of Feature Reduction --- p.54Chapter 3.4.2 --- Performance of Classification --- p.57Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60Chapter 3.5 --- Chapter Summary --- p.61Chapter 4. --- Conclusions --- p.63Reference --- p.6