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

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.

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.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.

This research clearly demonstrates the comparative performance study of Least Mean Square (LMS) adaptive and fixed Notch filter in terms of simulation results and different performance parameters (mean square error, signal to noise ratio and percentage root mean square difference) for removing structured noise (50 Hz line interference and its harmonics) and baseline wandering from electrocardiogram (ECG) signal. The ECG samples collected from the PhysioNet ECG-ID database are corrupted by adding structured noise and base line wandering noise. The simulation results and numerical performance analysis of this research clearly show that LMS adaptive filter can remove noise efficiently from ECG signal than fixed notch filter

<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.

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.

Electrocardiogram (ECG) is the graphical illustration of the heart’s electrical activity and it is acquired through linking electrodes to body surface. It is useful in determining cardiac diseases non-invasively. The general concerns in ECG signal processing is the restrain of noise. This paper has been encouraged by the necessary to recognize the ability of Savitzky-Golay (SG) filter for denoising the ST segment of ECG signals. It essentially implicates the extraction of essential cardiac components by refusing the background noise with the aid of filtering technique. The simulation is carried out using MATLAB and the experiments are performed on physionet database. Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Square Error (MSE) and Percentage Root mean square Difference (PRD) are used as performance evaluation metrics. This work uses SG filter to denoise the ECG signal and comparison is provided for SG filter with Median, Butterworth and SOS filters. The comparison results states the performance of the SG filter is better than that of the other filters.

Electrocardiogram (ECG) signal classification is an essential task to diagnose arrhythmia clinically. For effective ECG analyses, it has to be decluttered from embedded low and high frequency noise. Low frequency noise include baseline wander and high frequency noise include power line interference. We provide a comparative study for the task of baseline wander removal from ECG signals using different variants of Empirical Mode Decomposition, Median Filtering and Mean Median Filtering with a major emphasis on variational mode decomposition as it is a relatively new technique and much more robust towards noise. The comparison between the aforementioned techniques depicted that variational mode decomposition estimates better baseline as compared to other techniques in terms of pearson correlation, percentage root mean square difference and maximum absolute error. However, the time required to decompose the signal is relatively higher than the filtering techniques.

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.

In this paper, a transform based methodology is presented for compression of electrocardiogram (ECG) signal. The methodology employs different transforms such as Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT). A comparative study of performance of different transforms for ECG signal is made in terms of Compression ratio (CR), Percent root mean square difference (PRD), Mean square error (MSE), Maximum error (ME) and Signal-to-noise ratio (SNR). The simulation results included illustrate the effectiveness of these transforms in biomedical signal processing. When compared, Discrete Cosine Transform and Fast Fourier Transform give better compression ratio, while Discrete Wavelet Transform yields good fidelity parameters with comparable compression ratio.

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.

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.

This work presents a proficient approach of designing an M-band Near Perfect Reconstruction (NPR) Cosine Modulated Filter Bank (CMFB) where the NPR condition is built upon to satisfy the power complementary property and the pass-edge frequency is repetitively varied to adjust the filter-coefficients to reduce the reconstruction error occurred. Using this optimal value of the pass-edge frequency, the prototype filter is designed and the filter bank is considered from that single prototype filter. Thus the primary goal of designing the CMFB comes down to the issue of implementing efficient structure of this prototype filter. Now the prototype filter is designed with Interpolated Finite Impulse Response (IFIR) technique where numbers of hardware multipliers are reduced by cascading model and image suppression filter compared to that of single prototype filter and furthermore the designed filter coefficients are replaced with Canonical Signed Digit (CSD) to get lesser hardware complexity. To make the system practically realizable, an Electrocardiogram (ECG) signal is processed through this system and reconstructed back again. Some quality measurement parameters such as Signal-to-Noise Ratio (SNR), Mean Square Error (MSE) and Percentage Root Mean Square Difference (PRD) are analyzed to see the effectiveness of the CMFB.