A Power-Line Interference Canceler Based on Sliding DFT Phase Locking Scheme for ECG Signals

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.

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

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) 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.

Electrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal. This paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise. A considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®. This work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE).

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.

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

Real time monitoring and detection of abnormal heart rhythms, specifically through acquisition of electrocardiogram (ECG) signals for extended periods (24 to 48 hours) have been sought by many researchers. Abnormal ECG signals could appear any time in the day during regular patient activities. Spurious noise associated with motion and muscular contraction is usually coupled with the acquired ECG signal. Cancelling muscle contraction and motion artifacts from real time ECG signals is extremely vital for accurate ECG signal classification for analysis by trained medical specialists. The intent of this research was to develop a bi-electrode wireless acquisition system that captured and transmitted ECG signals and motion acceleration information to a host computer system for data capture and analysis. The designed wireless ECG system includes a 3-axis micro electromechanical (MEMS) accelerometer for motion detection.

The accurate diagnosis of cardiovascular conditions relies heavily on Electrocardiogram (ECG) signals, yet persistent interference challenges, including baseline wander, powerline interference, and muscle artifacts, compromise clinical accuracy. This study comprehensively explores cutting-edge preprocessing techniques aimed at addressing multifaceted challenges in ECG signal processing. Investigating noise removal, baseline correction, feature extraction, arrhythmia detection, and heart rate variability (HRV) analysis, we synthesize insights from recent research to provide a thorough understanding of current state-of-the-art methodologies. Each facet plays a crucial role in enhancing the reliability of ECG signals for accurate cardiovascular diagnoses. In a landscape where clinical accuracy is paramount, this review critically assesses advancements in signal processing techniques, shedding light on innovative strategies and potential breakthroughs.

The pre processing analysis is most important in the Electrocardiogram (ECG) signal. In order to preprocess the signal quality we used an efficient block based error normalized variable step size adaptive algorithm. Using this algorithm the ECG artifacts can be minimized. In this paper we considered power line Interference and baseline wander artifacts of ECG signal. We also obtained other algorithms from the block based error normalized algorithms: those are Sign Regressor, Sign Error and Sign Sign Block based algorithms, because of these algorithms give less computational complexity. The performance of the signal is evaluated and calculated signal to noise ratio, plotted convergence characteristics. By the observation of noise cancellation in power line interference the Sign Regressor variable step size algorithm performs better. The ability of the filter is checked by collecting ECG signal from MIT-BIH database.

This article introduces a new electrocardiogram (ECG) signal model based on geometric signal properties. Instead of the artificial functions used in common ECG models, the proposed model is based on the modelling of real ECG signals divided into time segments. Each segment has been modelled using simple geometrical forms. The final ECG signal model is represented by the sequence of parameters of the base functions. Parameter variations allow for the generation of different waveforms for each subsequent heartbeat without mixing up the PQRST waves order. Two basic models utilize slightly modified elementary functions, which are computationally simple. A combination of both models allows for the modelling of irregularities in the consecutive heartbeats of the specific ECG waveforms. Respiratory, noise, and powerline interference can be added in order to make the generated ECG signal more realistic. The model parameters are estimated by differential evolution optimization and a comparison between the modelled ECG and the acquired signal. The proposed models are tested by the database included in the LabVIEW Biomedical Toolkit and ECG records in the MIT-BIH arrhythmia database.

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.

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.

A signal-piloted linear phase filtering tactic for removing baseline wander and power-line interference from the electrocardiogram (ECG) signals is suggested. The system is capable of adjusting its parameters by following the incoming signal variations. It renders the processing of lesser samples by inferior order filters. The applicability is demonstrated by using the MIT-BIH ECG database. The precision of the approach is also studied regarding the signal-to-noise ratio (SNR). Results showed that the proposed method achieves a 2.18-fold compression gain and notable computational efficiency over conventional counterpart while securing an analogous output SNR. A comparison of the designed solution is made with the contemporary empirical mode decomposition with Kalman filtering and eigenvalue decomposition based tactics. Results show that the suggested method performs better in terms of output SNR for the studied cases.