Baseline Wander Noise Research Articles

Prediction of QT interval in ECGs through baseline wander removal and deep learning technique

Abstract Background In electrocardiograms (ECGs), the QT interval, calculated from the onset of the Q-wave to the end of the T-wave, reflects the repolarization status of the heart. Currently, the QT interval automatically measured in 12-lead electrocardiograms is inaccurately derived as the median value, rather than utilizing the more precise mean value of QT intervals from all leads. This approach fails to provide information on heterogeneous QT prolongation crucial for predicting arrhythmias, as it does not accurately represent the values of QT intervals in each lead. Purpose Traditional analysis of ECGs based on QT intervals necessitates direct interpretation by cardiologists, leading to potential errors in diagnosis. In response to this challenge, we present a model that integrates low-pass filtering and continuous wavelet transformation to eliminate baseline wander noise occurring during ECG measurements. Moreover, to account for temporal features, we incorporate Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to estimate QT intervals. Methods The dataset used in this study was the QT Database, comprising ECG data for 105 patients recorded at a sampling rate of 250Hz over a duration of 15 minutes. Each ECG was associated with annotations for two leads and information regarding the P, QRS, and T waves. We structured the dataset into a ratio of approximately 8:1:1, resulting in 51 patients for training (15,490 samples), 6 patients for validation (1,564 samples), and 7 patients for testing (1,682 samples). Results For the evaluation of deep learning, we selected Accuracy and F1-Score as metrics. We experimented with two configurations: the proposed model with two Bi-LSTM layers and an alternative with a single Bi-LSTM layer of 100 units. We also tested a similar recurrent neural network, GRU, with the same unit configuration. The results indicated that the method with two Bi-LSTM layers outperformed others in both Accuracy and F1-Score. Conclusion By incorporating CNN and Bi-LSTM, we conducted QT interval estimation, leveraging temporal features. This methodology facilitated the elimination of diverse noise types during ECG measurements, resulting in enhanced accuracy for QT interval estimation through the consideration of temporal ECG characteristics. In our future research endeavors, we plan to deploy this model for predicting arrhythmias, specifically targeting conditions like long QT syndrome and drug-induced QT prolongation.Methods and result

Denoising methods for removal of Baseline Wander, AWGN and power line interface noises in ECG signal: a comparative analysis

ABSTRACT The electrocardiogram (ECG) is one of the most significant signals in the field of biomedicine for the diagnosis of cardiac arrhythmia (CA). Due to non-stationary behaviour of the ECG signal, it is often interrupted with various noises, which cause difficulty in diagnosis. To solve the issues, the image denoising process helps the physicians make the perfect decision. In this paper, Denoising Techniques using Empirical Mode Decomposition (EMD) and Wavelet Transform for the removal of various noises like Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interface (PLI) noise at various concentrations. The proposed method has quality and good efficiency in Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RSME), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD).

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.

Accurate wavelet thresholding method for ECG signals

Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method’s ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.

ECG Signal Denoising Using an Improved Hybrid DWT-ADTF Approach.

The electrocardiogram signal (ECG) presents a fundamental source of information to consider for the diagnosis of a heart condition. Given its low-frequency features, this signal is quite susceptible to various noise and interference sources. This paper presents an improved hybrid approach to ECG signal denoising based on the DWT and the ADTF methods. The proposed improvements consist of integrating an adaptive [Formula: see text] parameter into the ADTF approach, combining a soft thresholding ADTF-based process with the DWT details, along with employing the mean filter to handle the baseline wandering noise. Furthermore, the proposed approach incorporates several denoising measures based on various proposed noise features, which have also been introduced in this approach. Several real noises collected from the Noise Stress Test Database (NSTDB), as well as several synthetic noises at different SNR levels, are proposed to ensure a thorough assessment of the proposed method's performance. The evaluation focuses on the SN Rimp, PRD, and MSE parameters, as well as the SINAD parameter as a diagnostic distortion measurement. Furthermore, a time complexity evaluation is proposed. The proposed approach demonstrated promising results compared to a recent hybridization of the DWT and ADTF methods, as well as recently published ECG signal denoising-based approaches in various real and synthetic noise cases using different statistical evaluation metrics. In the vast majority of the study cases, the proposed approach outperforms the compared methods in terms of statistical results for real and synthetic noises. Furthermore, compared to these methods, it provides a fairly low time complexity. This is consistent with the ambition of embedding this approach in low-cost hardware architectures.

Memory Classifiers for Robust ECG Classification against Physiological Noise.

The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural networks, are well known to underperform when subjected to small perturbations which would not pose a challenge to physicians. This is a hurdle that needs to be removed to facilitate wide-scale adoption. We find this to be true even for models trained using data-augmentation schemes.In this paper, we show that using memory classifiers it is possible to attain a boost in robustness using expert-informed features. Memory classifiers combine standard deep neural network training with a domain knowledge-guided similarity metric to boost the robustness of classifiers. We evaluate the performance of the models against naturally occurring physiological perturbations, specifically electrode movement, muscle artifact, and baseline wander noise. Our approach demonstrates improved robustness across all evaluated noises for an average improvement in F1 score of 3.13% compared to models using data augmentation techniques.Clinical relevance- This approach improves the robustness of deep learning methods in safety-critical medical applications.

Uji Kinerja Sistem Denoising Sinyal Jantung atau EKG dengan Menggunakan Algoritma Empirical Mode Decomposition (EMD)

Electrocardiograms (EKGs) are signals created by the electrical activity of the heart muscle and displayed on the EKG device's monitor. Using the EKG recording, the primary characteristics for diagnosing the status of the human heart can be determined. The death rate of heart patients can be reduced through early identification of cardiac problems. In ECG readings, it is frequently affected by several disruptions caused by muscle contractions and electrode movement. Numerous investigations on ECG signal denoising techniques have been undertaken earlier. This article examines the testing of the performance of EKGs using the denoising technique based on the Empirical Mode Decomposition (EMD) algorithm. In this work, many metrics were utilized to evaluate the ECG signal denoising technique: mean square error (MSE), mean absolute error (MAE), and signal-to-noise ratio (SNR). In this investigation, the ECG data was contaminated with noise from muscle artifacts (MA), additive Gaussian white noise (AWGN), electrode movement (EM), and baseline wander (BW). The noise-contaminated ECG signal is subsequently subjected to the denoising process. Calculate the MSE, MAE, and SNR values of the signal after it has been denoised. This study includes a scenario for testing three thresholding techniques with four distinct types of noise. The performance of the hard thresholding method is superior for all types of noise. MSE is produced by AWGN, which is 0.15, 0.28, and 9.9 dB. MA noise generates MSE, MAE, and SNR values of 0.4, 0.033, and 41.0 dB, respectively. The EM noise has an MSE of 0.010, an MAE of 0.04, and an SNR of 30.8 dB. The MSE produced by BW noise is 0.008; the MAE and SNR values were 0.0356 and 28.5, respectively.

Design of filters using current amplifiers for removal of noises from ECG signal

Biomedical technologies help countless patients in improving the quality of managing health. The latest trend in biomedical engineering includes tissue bioengineering, biosensors, enhanced bone regeneration; and bioinstrumentation. Electrocardiogram (ECG) a very popular tool in medical science records the electrical activity of the heart. Its interpretations & usefulness have created a revolution in Cardiology. It involves measuring the potential difference between two points or measuring the dipole moment of the heart when it contracts and relaxes. There are various noises present in the signal. In this paper, the authors have designed filters for the elimination of electromyography noise and baseline wander noise present in ECG signal. The filters are designed and simulated using various approaches namely operational amplifier, a two-stage operational amplifier (2SOA), an operational transconductance amplifier (gm amplifier). The theoretical values are evaluated and compared using simulated results. The theoretical and practical values agree with each other. Also, current amplifiers (2SOA and gm amplifier) results in better gain improvement in comparison to operational amplifier

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707, 10.7181, 16.2824, 0.9874 and 10.7245 for the proposed EMD-DWT approach, respectively, by removing AWGN noise. Similarly BW noise and PLI are removed from the ECG signal by calculating the same quality metrics. The proposed methodology has lower RMSE and PRD values, higher SNR, PSNR and CC values than the conventional methods.

ECG Noise Removal Using Modified Distributed Arithmetic Based Finite Impulse Response Filter

ECG monitoring is essential to support human life. During signal acquisition, the signals are contaminated by various noises that occur due to different sources. This paper focuses on Baseline wander and Muscle Artifact noise removal using Distributed Arithmetic (DA) based FIR filters. An area-efficient modified DA based FIR filter consists of LUT-less structure and used for noise removal. The performance of the modified DA based FIR filter is compared with the conventional DA FIR filter. An arbitrary real-time ECG record is taken from MIT-BIH database and Baseline Wander noise, Muscle artifact noises are taken from MIT-BIH noise stress test database. The performance of both filters is evaluated in terms of output Signal to Noise Ratio (SNR) and Mean Square Error (MSE). For Baseline wander noise removal, the modified DA based FIR filter produces high output SNR and also low MSE of 76.6% than the conventional filter. Similarly, for Muscle Artifact noise removal, it produces high SNR, and MSE is reduced to 73.8%. A modified DA based FIR filter is synthesized for the target FPGA device Spartan3E XC3s2000-4fg900 and hardware resource utilization is presented.

EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

Adaptive denoising of ECG using EMD, EEMD and CEEMDAN signal processing techniques

Using adaptive signal processing techniques denoising of ECG signal is performed which is obtained from physionet database. In this paper, the baseline wandering noise is removed using different adaptive techniques such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). All these algorithms are effectively used to decompose the noisy ECG signal into different Intrinsic Mode Functions (IMFs) and further these IMFs are filtered using low pass filtering method to extract the low frequency baseline component. The high frequency noise present in the reconstructed signal is reduced by further decomposing into IMFs using all the three methods. These IMFs are soft thresholded to remove the high frequency noise. The results obtained from the CEEMDAN outperform EMD and EEMD in extracting signal from noise. Further, distinct parameters such as skewnesscrest factor, RMS value and kurtosis are estimated for the reconstructed signal to analyse their behaviour.

SAMPLE ENTROPY ANALYSIS OF HEART RATE VARIABILITY IN RR INTERVAL DETECTION

Entropy is a robust method that is able to measure irregularities or the general behavior of the complex time series which could be continuously interact with many different and independent factors in time. This study aimed to investigate the sample entropy measurement of heart rate variability (HRV) for evaluating of 50Hz interference and baseline wander (BW) noise effects on RR interval. Three different synthetic electrocardiogram (ECG) signals were recorded using the simulator device. Sample Entropy (SampEn) values of full length and windowed length of data were calculated to track and identify RR intervals. It was found that adult normal sinus rhythm (NSR) signal without noise had the most regular and consistent results while adult ECG signal with BW noisy had the most irregular and inconsistent results. Furthermore, the BW noisy had more effect on irregularity ECG signal than 50 Hz interference. Consequently, the SampEn provided the measurement of irregularity and randomness of ECG data. However, it was found that the determination of RR intervals for classification and decision support systems was not practical in real-time analysis of HRV from raw ECG recordings because of noisy affect.

An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance.

Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.

A novel method to process surface electromyography signal for pedestrian lower limb motion pattern recognition

Surface electromyography (sEMG) signals mainly contain power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW) noise. These noises lead to the problems of poor feature extraction performance and low recognition rate. In this paper, we propose a novel sEMG signal processing method named filtering and self-enhancement algorithm with classical wavelet (FSECW) algorithm, which denoises interference noises of raw signals and improve the pedestrian motion pattern recognition rate. The proposed FSECW algorithm contains two core parts: in the first part, the original signal is reconstructed after four-layer wavelet decomposition. This part decreases the BW noise and enhances the active segment; in the other part, band-pass filtering and lifting wavelet transformation are used to reduce noises inside and outside the sEMG signal band. Then two signals from the above parts are multiplied. Thus, the enhanced filtered signal of the active segment is obtained. After feature extraction, the algorithm uses classical machine learning algorithm for motion pattern recognition. Experimental results show that the proposed FSECW algorithm does not need to set different thresholds for different data sets with the same motion pattern. Moreover, it has better adaptability to conversions of different motion patterns.

Fully Adaptive Denoising of ECG Signals Using Empirical Mode Decomposition with the Modified Indirect Subtraction and the Adaptive Window Techniques

Electrocardiogram (ECG) is one of the major methods for the diagnosis of heart malfunctions. ECG signals are susceptible to both high-frequency and low-frequency noises such as electromyography (EMG), power line interference (PLI) and baseline wander noises, respectively. These noises deteriorate the quality of ECG signals and challenge the proper identification of heart illnesses. In this article, we report an improved fully adaptive method for canceling high-frequency noises using the empirical mode decomposition (EMD) with the modified indirect subtraction and adaptive window techniques. As high-frequency noises are approximately of zero mean and lower-order intrinsic mode functions (IMFs) contain high-frequency noises, a statistical test is performed to determine whether a particular combination of IMFs has zero mean. First, to remove the PLI noise, the modified indirect subtraction technique is used. The sum of IMFs which are dominated by the noise is passed to a Butterworth band-pass filter, and the resultant filtered signal is subtracted directly from the noisy ECG signal. Then, the EMG noise is removed by an improved adaptive window-based noise reduction technique. By exploiting this technique, in contrast to common EMD-based methods, the duration of the QRS complex is computed regarding the location of the peak of the R wave and the widest possible QRS complex which makes the proposed technique applicable to all types of ECG signals. The quantitative results of simulations performed on several records from the MIT–BIH arrhythmia database prove the better performance of the proposed method than the compared methods at different noise levels.

Descendant adaptive filter to remove different noises from ECG signals

Electrocardiogram (ECG) signals are electrical signals generated corresponding to activity of heart. ECG signals are recorded and analysed to monitor heart condition. In initial raw form, ECG signals are contaminated with different types of noises. These noises may be electrode motion artefact noise, baseline wander noise and muscle noise also known as electromyogram (EMG) noise etc. In this paper, a descendent structure consists of adaptive filters is used to eliminate the three different types of noises (i.e., motion artefact noise, baseline wander noise and muscle noise). The two different adaptive filtering algorithms have been implemented; least mean square (LMS) and recursive least square (RLS) algorithm. The performance of these filters are compared on the basis of different fidelity parameters such as mean square error (MSE), normalised root mean squared error (NRMSE), signal-to-noise ratio (SNR), percentage root mean squared difference (PRD), and maximum error (ME) has been observed.

Descendant adaptive filter to remove different noises from ECG signals

Electrocardiogram (ECG) signals are electrical signals generated corresponding to activity of heart. ECG signals are recorded and analysed to monitor heart condition. In initial raw form, ECG signals are contaminated with different types of noises. These noises may be electrode motion artefact noise, baseline wander noise and muscle noise also known as electromyogram (EMG) noise etc. In this paper, a descendent structure consists of adaptive filters is used to eliminate the three different types of noises (i.e., motion artefact noise, baseline wander noise and muscle noise). The two different adaptive filtering algorithms have been implemented; least mean square (LMS) and recursive least square (RLS) algorithm. The performance of these filters are compared on the basis of different fidelity parameters such as mean square error (MSE), normalised root mean squared error (NRMSE), signal-to-noise ratio (SNR), percentage root mean squared difference (PRD), and maximum error (ME) has been observed.

Simulink implementation of RLS algorithm for resilient artefacts removal in ECG signal

Noise is the undesired signal which affects the desired signal. This noise has been a serious problem which is affecting the signals during transmission of information. In this project, two different noisy signals are considered they are speech signal and ECG signal. The speech signals are taken from the NOIZEUS database and ECG signals from Physio Net ECG database. The major noises affecting the ECG signal are baseline wander, electrode motion, power line interference, muscle artefact noises. The baseline wander noise which is caused due to patient movement, breathing and bad electrode contact to skin, electrode motion noise occurs when electrode moves away from the skin which leads to impedance changes resulting in variations in ECG, muscle artefact noise which is caused due to contraction of other muscles besides the heart. Simulink model is designed for cancelling the noise from the noisy signals. The adaptive algorithm that is chosen is the RLS algorithm because it has faster convergence rate when compared to other algorithms like LMS, NLMS and RLS. The Simulink model is tested for different cases to show that the model works efficiently and the performance can be observed from the mean square error obtained.