Synthetic Electrocardiogram Signals Research Articles

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

Employing Generative Networks for Synthetic Phonocardiogram and Electrocardiogram Signal Creation: A Privacy-Ensured Approach to Data Augmentation in Heart Diagnostics

The diagnosis of various cardiac conditions necessitates meticulous analysis of Phonocardiogram (PCG) and Electrocardiogram (ECG) signals. In light of this, artificial intelligence and machine learning, coupled with computer-assisted diagnostic techniques, have been progressively integrated into modern healthcare systems, facilitating clinicians in making crucial diagnostic decisions. However, the effectiveness of these deep learning applications hinges on the availability of extensive training data, which exacerbates the risk of privacy violations. In response to this dilemma, research into methodologies for synthetic patient data generation has witnessed a surge. It has been observed that most attempts to generate synthetic ECG and PCG signals focus on modeling the statistical distributions of the available real training data, a process known as Data Augmentation. Among the various data augmentation techniques, Generative Adversarial Networks (GANs) have gained significant traction in recent years. This paper conducts an in-depth exploration and evaluation of GANs, specifically Deep Convolutional GANs and Conditional GANs, for the generation of synthetic ECG and PCG signals.

Secure and resource-efficient communications for telemedicine systems

This paper designs and implements a secure and resource-efficient communication scheme for electrocardiogram (ECG) telemedicine systems. In the authentication and key agreement phase, a novel reusable Improved Juels-Sudan (IJS) fuzzy extractor with the Shared Random Bio-pool (SRB) technique is proposed to prevent the information leakage of personal biometrics. By exploiting the dynamical biometric model (DBM) for generating synthetic ECG signals, the reusable IJS fuzzy extractor could accomplish identity authentication, generation and distribution of keys, and periodic rekeying in a zero-leakage, user no-involvement and efficient way. In data encryption and transmission phase, a Task Migration Deployment Strategy (TMDS) is adopted to improve the efficiency of various resources. Experiments show that the scheme has high security, including information zero-leakage, low false acceptance rate (FAR) and false rejection rate (FRR), as well as saves various resources with a ratio of 385.8:1, which improves by at least 294.8% compared to the existing schemes.

A Simple Low-Cost Electrocardiogram Synchronizer.

Electrocardiogram (ECG) synchronization is useful to avoid the effects of cardiac motion in medical measurements, and is widely used in standard medical imaging. A number of medical equipment include embedded commercial synchronizers. However, the use of independent synchronization modules is sometimes needed when several non-integrated instruments are used, or in the development of new medical instruments and procedures. We present a simple low-cost ECG synchronizer module based on an Arduino controller board that converts the ECG signal into a transistor-transistor-logic (TTL) one, allowing real-time medical measurements triggered at specific phases of the cardiac cycle. The device and conversion algorithm developed is optimized in vitro using synthetic and human ECG signals, and tested in vivo on three swine specimens. Error rates during the in vivo testing stage remain below the 2% of the cycles in all animals and critical false positives are less than 1%, which is sufficient for most applications. Possible algorithm updates are discussed if its performance needs to be improved.

A Study on User Recognition Using the Generated Synthetic Electrocardiogram Signal

Electrocardiogram (ECG) signals are time series data that are acquired by time change. A problem with these signals is that comparison data that have the same size as the registration data must be acquired every time. A network model of an auxiliary classifier based generative adversarial neural network that is capable of generating synthetic ECG signals is proposed to resolve the data size inconsistency problem. After constructing comparison data with various combinations of the real and generated synthetic ECG signal cycles, a user recognition experiment was performed by applying them to an ensemble network of parallel structure. Recognition performance of 98.5% was demonstrated when five cycles of real ECG signals were used. Moreover, 98.7% and 97% accuracies were provided when the first cycle of synthetic ECG signals and the fourth cycle of real ECG signals were repetitively used as the last cycle, respectively, in addition to the four cycles of real ECG. When two cycles of synthetic ECG signals were used with three cycles of real ECG signals, 97.2% accuracy was shown. When the last third cycle was repeatedly used with the three cycles of real ECG signals, the accuracy was 96%, which was 1.2% lower than the performance obtained while using the synthetic ECG. Therefore, even if the size of the registration data and that of the comparison data are not consistent, the generated synthetic ECG signals can be applied to a real life environment, because a high recognition performance is demonstrated when they are applied to an ensemble network of parallel structure.

An effective heterogeneous whole-heart mathematical model of cardiac induction system with heart rate variability.

The main objective of this research work is to develop an effective mathematical model of cardiac conduction system using a heterogeneous whole-heart model. The model is in the form of a system of modified Van der Pol and FitzHugh-Nagumo differential equations capable of describing the heart dynamics. The proposed model extends the range of normal and pathological electrocardiogram (ECG) waveforms that can be generated by the model. The effects of the respiratory sinus arrhythmia (RSA) and the Mayer waves (MW) are both incorporated to modulate the intrinsic frequency of the main oscillator that represents the sinoatrial node. Also, three pathological conditions are incorporated into the model. The heart rate variability (HRV) phenomenon is incorporated into the synthetic ECGs produced which yields valuable information about the cardiovascular health and the performance of the autonomic nervous system. The spectral analysis of the generated RR tachogram delivers power spectrums that resemble those obtained from real recordings. Also, the proposed model generates synthetic ECGs that characteristic the three considered pathological conditions, namely, the tall T wave, the ECG with U wave, and the Wolff-Parkinson-White syndrome. In general, the significance of this research work is in developing a mathematical model that represents the interactions between different pacemakers and allows analysis of cardiac rhythms. To show the effectiveness and the accuracy of the presented model, the results are compared to published results. The proposed model can be a useful tool to study the influences of different physiological conditions on the profile of the ECG. The synthetic ECG signals produced can be used as signal sources for the assessment of diagnostic ECG signal processing devices.

Empirical Mode Decomposition, Viterbi and Wavelets Applied to Electrocardiogram Noise Removal

The electrocardiogram (ECG) signal is generally used as a cardiovascular disease diagnostic tool. The accuracy of the diagnosis is directly related to the quality of the ECG signal, which can be corrupted by several sources of noises such as, for example, baseline wanders and power line interference. This paper proposes a new ECG denoising methodology based on wavelets, empirical mode decomposition (EMD), and Viterbi algorithm. The EMD decomposes the signal in intrinsic mode functions (IMFs), then each one of these IMFs is processed by the discrete wavelet transform through a decision process based on the Viterbi algorithm. We apply the proposed method to a synthetic ECG signal and three real ECG signals. The simulations results show that this novel methodology outperforms denoising schemes based on wavelets, empirical mode decomposition, and total variation.

A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC

An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding.To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.

Generating electrocardiogram signals by deep learning

Given the importance of a diverse and vast amount of realistic and labeled electrocardiogram (ECG) signals in improving the performance of biomedical signal processing algorithms, and the situation of severe lack of the signals, three generative models based on deep learning are introduced for the generation of ECG signals: The WaveNet-based model, the SpectroGAN model, and the WaveletGAN model. The WaveNet-based model adopts μ-law companding transformation as a preprocessing method and then is followed by a sequence of convolutional layers with dilation; SpectroGAN and WaveletGAN use short-term Fourier transform (STFT) and stationary wavelet transform (SWT) respectively to obtain suitable input form for the generative adversarial networks (GAN). Our proposed models are capable of generating ECG signals containing three different heartbeat types: normal beat, left bundle branch block beat and right bundle branch block beat. The synthetic ECG signals generated by our models are more realistic since deep artificial neural networks can discover intricate structure and characteristics of real ECG signals instead of manually setting specific parameters for synthesis. Besides, ECG signals produced by one of our proposed models could be naturally continuous and be up to more than 20 seconds. Furthermore, we first provide an evaluation approach for quantitatively demonstrating the performance of ECG generative models. The study demonstrates that deep learning is a feasible and effective method for ECG generation. Our proposed ECG generative models can be utilized to assess biomedical signal processing algorithms so as to improve their performance in clinical trials.

Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Electrocardiogram (ECG) signal quality enhancement is a crucial task in the computer-based automated processing system. In this study, a dictionary learning (DL)-based sparse representation framework is presented for ECG signal enhancement through denoising. Unlike, traditional filtering techniques, the proposed method removes both low- and high-frequency noises for complete enhancement of the signal quality. The frequency localised DL-based sparse representation approach is applied to remove both baseline wander and power-line interference. The time-localised and signal characteristics based DL-based sparse representation scheme is employed for extraction of clean ECG components from white Gaussian noise and muscle artefact added noisy ECG record. Both qualitative and quantitative performance analyses are carried out using the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database, QT database and synthetic ECG signals. The efficacy of the proposed method is compared with the existing ECG denoising approaches using standard performance metrics: signal-to-noise ratio, root-mean-square error and percentage RMS difference. It is observed through the detailed study and analysis that the proposed approach outperforms the existing ones and can be served to further enhance the overall quality of ECG in a computer-based automated medical system.

A comparative study between normal electrocardiogram signal and those of some cardiac arrhythmias based on McSharry mathematical model

In this paper, synthetic electrocardiogram signals (SECG) of eight cardiac arrhythmias (sinus bradycardia, junctional bradycardia, tachycardia, flutter, atrial extrasystole, ventricular extrasystole, left branch block and right branch block) are obtained numerically by solving the McSharry mathematical model (2003) based on three coupled ordinary differential equations with the fourth-order Runge-Kutta method. They are compared with normal electrocardiogram signal. Indeed, visual analysis of a section of electrocardiogram (ECG) signals of these arrhythmias was used to suggest suitable values for the parameters in the McSharry mathematical model. Results from numerical simulation showed a good agreement between the simulation results and the real cardiac arrhythmias ECG signals.

Periodically kicked network of RLC oscillators to produce ECG signals

We propose a simple model of the electrical activity of the heart that reproduces realistic healthy electrocardiogram (ECG) signals. The model consists of two RLC linear oscillators periodically kicked by impulses of the main pacemaker with the frequency rate of a real heart. In the proposed model, one oscillator represents the atria, another represents the ventricles, and an electrical cardiac conduction system is included using a coupling capacitor, which can be either unidirectional or bidirectional. The network of the two capacitively coupled oscillators is periodically kicked by the main pacemaker to introduce the periodic forcing of limit cycles into the system; a time delay is introduced to represent the electrical transport delay from atria to ventricles. In this manner, healthy synthetic ECG signals are obtained by combining the signals of the currents of the oscillators. We show that an analytical solution of the model can be obtained when a single impulse is applied. From this, by the superposition principle, a solution with an impulse train is obtained. Note that analytical treatment is a feature not available in current cardiac oscillator models.

Performance Investigation of Marginalized Particle-Extended Kalman Filter under Different Particle Weighting Strategies in the Field of Electrocardiogram Denoising.

Background:Recently, a marginalized particle-extended Kalman filter (MP-EKF) has been proposed for electrocardiogram (ECG) signal denoising. Similar to particle filters, the performance of MP-EKF relies heavily on the definition of proper particle weighting strategy. In this paper, we aim to investigate the performance of MP-EKF under different particle weighting strategies in both stationary and nonstationary noises. Some of these particle weighting strategies are introduced for the first time for ECG denoising.Methods:In this paper, the proposed particle weighting strategies use different mathematical functions to regulate the behaviors of particles based on noisy measurements and a synthetic ECG signal built using feature parameters of ECG dynamic model. One of these strategies is a fuzzy-based particle weighting method that is defined to adapt its function based on different input signal-to-noise ratios (SNRs). To evaluate the proposed particle weighting strategies, the denoising performance of MP-EKF was evaluated on MIT-BIH normal sinus rhythm database at 11 different input SNRs and in four different types of artificial and real noises. For quantitative comparison, the SNR improvement measure was used, and for qualitative comparison, the multi-scale entropy-based weighted distortion measure was used.Results:The experimental results revealed that the fuzzy-based particle weighting strategy exhibited a very well and reliable performance in both stationary and nonstationary noisy environments.Conclusion:We concluded that the fuzzy-based particle weighting strategy is the best-suited strategy for MP-EKF framework because it adaptively and automatically regulates the behaviors of particles in different noisy environments.

A wavelet-based method for power-line interference removal in ECG signals

Abstract Introduction The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT). In order to choose the wavelet function, several experiments were performed with synthetic signals with worse Signal-to-Noise Ratio (SNR). Results Considering the relative error metrics and runtime, the best wavelet function for denoising was Symlet 8. Twenty synthetic ECG signals with different features and eight real ECG signals, obtained in the Physionet Challenge 2011, were used in the experiments. Results show the advantage of the proposed method against thresholding and notch filter techniques, considering classical metrics of assessment. The proposed method performed better for 75% of the synthetic signals and for 100% of the real signals considering most of the evaluation measures, when compared with a thresholding technique. In comparison with the notch filter, the proposed method is better for all signals. Conclusion The proposed method can be used for PLI removal in ECG signals with superior performance than thresholding and notch filter techniques. Also, it can be applied for high frequencies denoising even without a priori frequencies knowledge.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform

BackgroundSince the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician’s correct decision on patients.MethodsThe dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance.ResultsA set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal.ConclusionThe simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement.

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).

Adaptive Spectro-Temporal Filtering for Electrocardiogram Signal Enhancement.

Advances in low-cost portable electrocardiogram (ECG) devices have opened doors for numerous new applications, including fitness tracking, remote health, and peak athletic performance monitoring, to name a few. Many such devices, however, have been shown to be highly contaminated by movement and/or muscle contraction artifacts, which, in turn, can lead to erroneous heart rate and heart rate variability (HRV) analyses. Here, we propose a new denoising method based on adaptive spectro-temporal filtering for ECG enhancement. The algorithm relies on the so-called modulation spectral signal representation, which is shown to accurately separate ECG and noise components. The proposed method was tested on synthetic ECG signals corrupted with varying levels of recorded noise and on long-term bedside noisy ECG recordings. Gains over a state-of-the-art wavelet-based denoising algorithm were achieved, particularly for very noisy scenarios. Overall, the proposed algorithm achieved a 61.8% gain in signal-to-noise ratio improvement, a three times reduction in average heart rate measurement error, and a 15% reduction in HRV measurement error relative to the benchmark, thus suggesting that it is an ideal candidate for ECG-based fitness/athletic monitoring applications.

A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography.

Long-term electrocardiogram (ECG) signals might suffer from relevant baseline disturbances during physical activity. Motion artifacts in particular are more pronounced with dry surface or esophageal electrodes which are dedicated to prolonged ECG recording. In this paper we present a method called baseline wander tracking (BWT) that tracks and rejects strong baseline disturbances and avoids concurrent saturation of the analog front-end. The proposed algorithm shifts the baseline level of the ECG signal to the middle of the dynamic input range. Due to the fast offset shifts, that produce much steeper signal portions than the normal ECG waves, the true ECG signal can be reconstructed offline and filtered using computationally intensive algorithms. Based on Monte Carlo simulations we observed reconstruction errors mainly caused by the non-linearity inaccuracies of the DAC. However, the signal to error ratio of the BWT is higher compared to an analog front-end featuring a dynamic input ranges above 15 mV if a synthetic ECG signal was used. The BWT is additionally able to suppress (electrode) offset potentials without introducing long transients. Due to its structural simplicity, memory efficiency and the DC coupling capability, the BWT is dedicated to high integration required in long-term and low-power ECG recording systems.

Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.