A review on ECG filtering techniques for rhythm analysis

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosis

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.

Electrocardiogram (ECG) signals are vital in diagnosing various cardiac abnormalities, yet they are often corrupted by artifacts such as powerline interference (PLI), muscle noise, and baseline wander (BW), which can obscure important clinical information. The present research explores a novel approach for multiple artifacts removal from ECG signals using cascading of multistage least mean square (LMS) adaptive FIR filter. The proposed method employs a cascade of LMS adaptive FIR filters, each optimized for targeting specific artifacts found in the ECG signal. By iteratively adapting filter coefficients based on the input signal and a reference signal obtained from the preceding stage, the proposed cascaded LMS adaptive FIR filter effectively suppresses different artifacts while preserving the integrity of the ECG signal. The proposed algorithm’s performance has been analyzed by evaluating the mean square error (MSE) value and signal-to-noise ratio (SNR) of the filtered ECG, and evaluation conducted on synthetic and real-world ECG datasets demonstrates the efficacy of the proposed method in reducing various types of artifacts, thereby reliability of ECG signal interpretation and enhancing the quality. This method holds promise for improving the precision of cardiac monitoring and diagnosis in clinical settings. The proposed model is simulated on MATLAB to measure the performance parameter which demonstrates the validation of model.

In recent years, Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Human Computer Interface (HCI), stress and emotional states assessment, etc. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. In this work, the stroop color word test based mental stress inducement have done and ECG signals are acquired from 10 female subjects in the age range of 20 years to 25 years. We have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. Three wavelet functions (db4, coif5 and sym7) and four different thresholding methods are used to denoise the noise in ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively. Four different performance measures were considered to select the appropriate wavelet function and thresholding rule for efficient noise removal methods such as, Signal to Interference Ratio (SIR), noise power, Percentage Root Mean Square Difference (PRD) and finally periodogramof Power Spectral Density (PSD). The experimental result shows the coif5 wavelet andrigrsurethresholding rule is optimal for unknown Signal to Noise Ratio (SNR) in the real time ECG signals.

Present paper deals with design and development of digital FIR filters. Heart diseases, which are one of the death reasons, are among the several serious problems in this century and as per the latest survey, 60% of the patients die due to Heart diseases. Electrocardiogram (ECG) signal plays a vital role in the monitoring and diagnosis of the health conditions of human heart. The basic ECG signal has the frequency range from .5Hz to 100Hz. The most significant noises that corrupt ECG signal are power line interference and baseline wanders. It becomes difficult for the Specialist to diagnose the diseases if artifacts are present in the ECG signal. Designing digital filters to reduce these noises is very important in ECG signal processing. In this work a digital FIR filter for reducing 50Hz power line noise in ECG signal is designed and implemented with different windowing methods as of Hamming, Kaiser, and Chebyshev. The work has been done in MATLAB environment. The result obtained for all FIR filters are compared by comparing the waveforms of the original and filtered ECG signals. The filter which gives the best results is the one using Chebyshev Window.

An electrocardiogram (ECG) signal may be affected by different types of disturbances/noises e.g. Power Line Interference (PLI), baseline wander, motion artifacts etc. Removing such disturbing signals from an ECG signal is the key to its accurate analysis leading to diagnosis of potential disease(s). In this paper, we present State Space Recursive Least Squares (SSRLS) filter based method for removal of 50Hz PLI noise from an ECG signal. The results are encouraging when compared with notch filter of varying attenuation levels. Under varying PLI noise levels, the SSRLS filter is seen to show superior performance as compared to notch filter. This work is part of National Information & Communication Technologies Research and Development (ICT R&D) fund project done in collaboration with National Institute of Heart Diseases (NIHD), Pakistan.

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition

Electrocardiogram (ECG) signals are susceptible to noise and interference from the external world. This paper presents the reduction of unwanted 50 Hz power line interference in ECG signal using multi-order adaptive LMS filtering. The novelty of the present method is the actual hardware implementation for power line interference removal. The design of adaptive filter is carried out by the simulink-based model and hardware-based design using FPGA. The performance measures used are signal to noise ratio (SNR), PSNR, MSE and RMSE. The novelty of the proposed method is to achieve better SNR by careful selection of the filter order using hardware.

Electrocardiogram (ECG) signal enhancement is necessary in telemedicine. In such ECG monitoring systems, noises like muscle artifacts, electrode motion and baseline wander are often embedded in the ECG signals during acquisition and transmission. In this study, a novel method is proposed for the ECG signal enhancement based on the finding that ECG signals extracted over a big data share significant similarities in the morphology for a particular person. We construct a guided filter and reform it by a Butterworth high-pass filter. The Butterworth high-pass filter is utilized to remove the baseline wander. The advantageous edge-preserving guided filter is then applied to remove the rest noise, of which frequencies are between the ECG signals. Very promising results with high accuracy and the edge-preserving features have been achieved in the comparative experiments. We evaluated the proposed denoising method using ECG signals from the MIT-BIH Arrhythmia database and the Noise Stress Test database. The experimental results demonstrate that the proposed method achieves better signal-to-noise ratio (SNR) and lower root mean square error (RMSE) when compared to the wavelet with subband dependent thresholding (WT-Subband), Back Propagation Neural Network and Stockwell transform methods. Using the proposed method, the average output SNR ranges from 8.57 to 19.28 dB, and the average RMSE is less than 0.41.

A power-line interference (PLI) removal method is proposed based on a previously published sliding discrete Fourier transform (SDFT) phase locking scheme (PLL), which extracts the nonstationary sinusoids from the electrocardiogram (ECG) signal. The proposed PLI canceler consists of SDFT PLL as a vital element, which can track the variation in center frequency of the PLI either 50 or 60 Hz. The PLI canceler involves the adaptive sampling frequency control in which the sampling frequency of SDFT filter is adaptively adjusted according to the center frequency variation of the interference. Since the in-phase and quadrature components of SDFT filter can provide instantaneous sinusoidal and cosinusoidal interference signals, the SDFT PLL is capable of tracking amplitude, phase, and frequency of the interference. Additional resonators of different bin indices are augmented with SDFT PLL to handle the dominant odd harmonics of PLI. Furthermore, the SDFT PLL is cascaded with another SDFT bin to eliminate the baseline wander present in the real-time ECG signal. The adaptive PLI canceler based on SDFT PLL offers the tracking capability of variant PLI, attenuation of 40 dB, less acquisition time of 0.2 s for a variant PLI of ±5 Hz, removal of dominant odd harmonics, and baseline wander with expense of sampling frequency. Experimental results prove the efficacy of the proposed method in nonstationary sinusoidal interference extraction. Experimental investigation on SDFT PLL using field programmable gate array demonstrates the feasibility of digital implementation of the proposed algorithm.

<title>Abstract</title> Electrocardiogram (ECG) signals are critical to identify various cardiac abnormalities. However, the accuracy of ECG-based clinical interpretations can be significantly compromised due to contamination from various artifacts. Traditional ECG denoising methods often struggle to maintain the balance between noise suppression and preservation of essential ECG characteristics. This study presents, for the first time, a deep learning (DL)-based autoencoder (AE) for robust ECG denoising and artifact cancellation and automated ECG diagnosis and classification. Unlike traditional methods, AEs are designed to learn efficient data representations, making them well-suited for distinguishing between clean signals and noise without extensive computational demands. The proposed approach leverages the PTB Diagnostic ECG Database (PTBdb) and introduces various types of noise derived from the MIT-BIH Noise Stress Test Database, including muscle artifacts (MA), electrode motion artifacts (EM), baseline wander (BW), and their combinations. The proposed AE model is trained to eliminate these noise components effectively while preserving the critical morphological features of the ECG signals. To assess the performance of the introduced method, comprehensive experiments are performed to compare clean, noisy, and AE denoised ECG signals. The AE's performance is further benchmarked against classical denoising techniques, assessing key metrics such as signal to noise ratio (SNR), mean square error (MSE), root mean square error (RMSE) and a percent root-mean square difference (PRD). The results elucidate that the introduced AE-based denoising framework not only outperforms conventional techniques in noise suppression but also enhances the reliability of ECG signal interpretation, making it an effective solution for implementation in clinical settings.

An adaptive filter is the utmost essential filter castoff in statistical signal dealing. The fine-tuning of the filter factor in relation to the response signal is the adaptive filter's key feature due to fewer calculations, Least Mean Square (LMS) adaptive filters are widely used to remove noise from Electrocardiograms (ECG). The adaptive filters are realized as signal processing algorithms in Digital Signal Processors (DSPs) or in VLSI Signal Processors (VSPs). The technique provides a way to create a folded adaptive lattice LMS filter, which requires less hardware than an adaptive lattice filter. Folding is an algorithm that uses a time scheduling technique that combines arithmetic operations into one operation which reduces Register and silicon chip areas. The design and implementation of a folded lattice adaptive filter remove Power Line Interference (PLI) noise from ECG signals. The MATLAB Xilinx System Generator tool is used to design the Adaptive Lattice LMS Filter and Folded Adaptive Lattice LMS Filter with Folding Order K = 2 and K = 4 and realized in the Virtex 5 FPGA KIT. The results of the folded architecture show that the area is reduced for K = 2 and K = 4 by 82.60% and 91.05%, respectively compared with a normal adaptive lattice filter.

Background:Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed.Methods:In the proposed work, the improved variational mode decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method.Results:The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db.Conclusion:The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.

This paper discusses the circuit comparison of the electrocardiogram (ECG) heart rate detector for wearable biomedical devices. In this work, the QRS complex is used to calculate heart rate, representing the main component of the ECG signal. In order to achieve a high level of accuracy by the detector, the measured ECG signal must be free of noise. Typically, such noise originates from power line interferences and baseline wandering. To eliminate these noises, the ECG signal must be processed using the signal processing method. The ECG detection process can be divided into a preprocessing stage, which eliminates noise from the raw ECG signal, and the feature extraction stage, which extracts the QRS complex for heart rate calculation. Based on our analysis, the trade-off between complexity and accuracy in ECG signal architecture is important when designing a high-efficiency system.