Comparative Study on R-peak Detection over Noisy and Denoised ECG Signal using Wavelet Transform

Abstract: Motion interference is a major issue in the process of acquiring electrocardiogram (ECG) signals. It introduces noise and artifacts into the ECG signal, causing signal distortion and deformation, thus affecting the accuracy of subsequent analyses and diagnoses. This study introduces a deep learning-based model for motion artifact removal in ECG signals, namely MSCT, which combines Multi-Scale Convolution with Transformer encoder to extract the local and global features of ECG signals and motion artifacts. The comparative experiments are conducted on the MIT-BIH arrythmia database which is contaminated with the motion artifact from MIT-BIH noise stress test database. Our MSCT model shows significantly higher Cosine Similarity and output signal-to-noise ratio (SNR), and lower Root Mean Square Error (RMSE) and Multi-Scale Entropy Based Weighted Percentage Root-mean-square Difference (MSEWPRD), compared with that of the three previous methods on the motion artifact-corrupted data with various input SNR (-6 dB, 0 dB and 6 dB). Especially, when the input SNR is -6 dB, the proposed MSCT achieves the results 6.15±2.83%, 0.25±0.14 mV, 0.91±0.08, 9.20±3.74 dB for MSEWPRD, RMSE, Cosine Similarity and SNR, respectively. All the four metrics, including quantitative and qualitative metrics, have demonstrated the superiority and robust of the MSCT model proposed in this study. Furthermore, the time-domain and frequency-domain plots of a randomly selected denoising segment at -6 dB effectively showcase the superior performance of our denoising method.

<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.

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

Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based technique for denoising EEG signals. These methods are trained, tested and evaluated on different synthetic and real ECG datasets taken from the MIT PhysioNet database and for different simulation conditions (i.e. various lengths of the ECG signals, single or multiple records). The results show the CNN model is a performant model that can be used for off-line denoising ECG applications where it is satisfactory to train on a clean part of an ECG signal from an ECG record, and then to test on the same ECG signal, which would have some high level of noise added to it. However, for real-time applications or near-real time applications, this task becomes more cumbersome, as the clean part of an ECG signal is very probable to be very limited in size. Therefore the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest). Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal.

In this work, we describe a methodology to probabilistically estimate the R-peak locations of an electrocardiogram (ECG) signal using a particle filter. This is useful for heart rate estimation, which is an important metric for medical diagnostics. Some scenarios require constant in-home monitoring using a wearable device. This poses a particularly challenging environment for heart rate detection, due to the susceptibility of ECG signals to motion artifacts. In this work, we show how the particle filter can effectively track the true R-peak locations amidst the motion artifacts, given appropriate heart rate and R-peak observation models. A particle filter based framework has several advantages due to its freedom from strict assumptions on signal and noise models, as well as its ability to simultaneously track multiple possible heart rate hypotheses. Moreover, the proposed framework is not exclusive to ECG signals and could easily be leveraged for tracking other physiological parameters. We describe the implementation of the particle filter and validate our approach on real ECG data affected by motion artifacts from the MIT-BIH noise stress test database. The average heart rate estimation error is about 5 beats per minute for signal streams contaminated with noisy segments with SNR as low as -6 dB.

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.

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.

The electrocardiogram (ECG) signals provide information for making decisions about different kinds of heart diseases. During decades various approaches have been developed to denoise ECG data and extract useful features, although further increase in the accuracy is required. In this paper, we view the ECG signal as a quasi periodic process and employ the unbiased finite impulse response (UFIR) smoother on optimal horizons. It is shown that the UFIR smoother applied to a harmonic ECG model performs better than that recently developed for polynomial ECG models. Extensive investigation are provided for diverse ECG data. The results are compared in terms of the mean square error and signal-to-noise ratio.

Highlights. The article presents a novel and unique method for assessment of left ventricular systolic disfunction using electrocardiography and photoplethysmography data. This method will improve and simplify the detection of cardiovascular diseases.Aim. To evaluate left ventricular (LV) systolic function using electrocardiogram (ECG) and photoplethysmogram (PPG) signals recorded by a single-channel ECG and PPG-based monitor.Methods. The prospective study included 489 patients over 18 years old with various cardiovascular diseases. All participants underwent echocardiography to determine the main indicators of LV systolic function: LV ejection fraction (EF), LV outflow tract velocity time integral (LVOT VTI), and global longitudinal strain (GLS). Moreover, all patients underwent 1-lead ECG and PPG recording using a single-channel ECG and PPG-based monitor (CardioQvark). The obtained data were analyzed, and ROC curve analysis was performed.Results. We have identified ECG and PPG parameters associated with a decrease in LV contractile function. During the analysis, the ECG, T-wave amplitude (TA) and RonsF parameters showed the highest diagnostic accuracy. With EF below 55%, the area under the ROC curve (AUC) was 0.822, sensitivity (Se) 80%, specificity (Sp) 69% in EF below 55% in TA; in RonsF AUC was 0.743, Se 81%, Sp 77%. With EF below 40%, AUC was 0.915, Se 85%, Sp 83% in TA, and in RonsF AUC was 0.844, Se 82%, Sp 82%. Diagnostic accuracy of ECG signals in case of LVOT VTI lower than 16 cm was measured: TA (AUC 0.755, Se 82%, Sp 70%), RonsF (AUC 0.620, Se 77%, Sp 72%). PPG signals were not significantly associated with reduced EF; however, the pulse wave parameters were associated with lower LVOT VTI: in DP-B0 AUC was 0.687, Se 71%, Sp 74%. The combination of ECG and PPG signals was significantly associated with EF below 40% (RonsF * DP-SEP (AUC 0.877, Se 86%, Sp 85%). ECG and PPG signals were not associated with LV GLS.Conclusion. Assessment of LV systolic function can be performed by analyzing ECG and PPG signals recorded using a portable single-channel CardioQvark monitor.

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.

Denoising of ECG Signals Using Wavelet Transform and Principal Component Analysis

In the areas of biomedical and healthcare, electrocardiogram (ECG) signal analysis is one of the major aspects of research. The accuracy in the detection of subtle characteristic features in ECG is of great significance. This paper deals with an algorithm based on hybrid linearization and principal component analysis for ECG signal denoising and R-peak detection. The ECG data have been taken from the MIT-BIH Arrhythmia Database for performance evaluation. The signal is denoised by applying the hybrid linearization method, which is an arrangement of the extended Kalman filter along with discrete wavelet transform, and then principal component analysis is employed to detect R waves and the QRS complex. The reported work has been implemented in the MATLAB environment for 25 different ECG records yielding 99.90% sensitivity, 99.97% positive predictivity, and a detection error rate of 0.120%. The achieved performance outperforms the recent research done in the area of interest.

Cardiovascular diseases (CVDs) are a dominant cause of death globally, taking an estimated 17.9 million lives each year. For countries with a huge population, like India with a high mortality rate, lack of emergency medical services (EMS) infrastructure worsens the initial problem at hand; due importance should be given to research and prototyping in portable health care. This chapter presents the design of a noninvasive, portable, and wearable device that is provided with an emergency SOS button and has the ability to acquire three-lead single-channel electrocardiogram (ECG) signals from the user. The ECG sensing block is developed on the AD8232 chip platform using special signal filtering and conditioning circuitry. When the SOS button is pressed, the system wakes up from idle, starts gathering the ECG signal data from the user, and transmits it in real time to the user's mobile phone through a Bluetooth module using serial communication. Simultaneously, the user's location is accessed using the inbuilt GPS functionality of the mobile phone as well as a GPS module onboard the wearable device. The GPS module serves as a fail-safe mechanism in case there are any issues with the internet or Bluetooth connectivity of the mobile phone. This data, comprising the user's ECG signals and the precise location, is relayed through a tailor-made mobile application to a Cloud-based patient database, which can be further accessed by emergency healthcare professionals during a medical emergency. The primary objective behind this system is to reduce the critical time for a user to get treatment in case of an emergency by providing the real-time location of the user to the medical professionals, so they can navigate quickly. Along with that, it also records ECG signals during trauma, which proves extremely beneficial for preliminary diagnosis as well as for the further treatment of the patient. Our system is assessed through technical verification, clinical testing, and conduction of a user survey, which have provided significant results and indicated positive feedback from our potential user base. The system provides a horizontal location accuracy between the range of 10 and 50 meters while providing the user's real-time location. Along with this location information, the lifesaving data consisting of the user's ECG signals and the real-time location is transmitted with a latency of 40–60 seconds to the medical professionals' mobile phones. The structure of the ECG signal provided to the medical professionals is morphologically similar to a medical-grade ECG signal. These results indicate that the system has an edge in reliability, accuracy, and convenience over the currently used wearable or portable monitoring systems. This system is a lifesaving link between an at-risk patient and EMS and medical professionals.

This paper investigates the application of the newly proposed Kronecker-based method for the reconstruction of compressively sensed electrocardiogram (ECG) signals. By applying the Kronecker-based method, ECG signal acquisition is done in smaller lengths. Collection of smaller length of ECG signals leads to fewer arithmetic operations during the compression phase. Instead of recovering individually in smaller lengths, recovery is done over several concatenated segments. This newly proposed recovery method improves the quality of the reconstructed signal when compared to the traditional recovery done without concatenation. Two random measurement matrices, namely the Gaussian and the Bernoulli matrices, are considered as sensing matrices in this study and the methodology is evaluated using 10 ECG signals acquired from the MIT arrhythmia database. The random Bernoulli matrix is found to provide better quality of recovered compressed ECG signal even with the traditional compressive recovery techniques. Recovery by the newly proposed Kroneckerbased method results in higher SNR in all the ECG signals when the compression ratio (CR) is 25% or 50% and when the CR is 75%, improvement is observed in majority of the ECG signals. Lower CRs provide better reconstruction than higher CRs. The Kronecker-based recovery method may be useful for wearable ECG devices.