Electrocardiogram Signal Enhancement Research Articles

Wavelet-domain group-sparse denoising method for ECG signals

In this paper, a group-sparse signal denoising approach is proposed, and both non-convex regularization and sparsity characteristics in wavelet domain are incorporated to estimate the electrocardiogram (ECG) signals with noise. To strongly promote wavelet sparsity, a parameterized non-convex penalty function is introduced, and the interval for the parameter is identified to guarantee the strict convexity of the total cost function. To retain the details of ECG signals, all the wavelet coefficients are estimated by minimizing certain single objective function, and thus the insignificant coefficients that do not survive wavelet thresholding can be maintained. The algorithm is solved based on the majorization–minimization optimization method, the alternating direction method of multipliers, and proximal method. The real collected ECG signals and MIT-BIH arrhythmia database are used to evaluate the effectiveness of the proposed wavelet-domain group-sparse method (WDGS) for ECG signal enhancement. By qualitative and quantitative analysis, it shows that our method can effectively suppress the undesired noise and keep the important morphology of ECG signals.

Qualitative and Quantitative Performance Comparison of ECG Noise Reduction and Signal Enhancement Method based on Various Digital Filter Designs and Discrete Wavelet Transform

Electrocardiogram (ECG) is a graphical representation and bio-signal recording of cardiac electrical activity. It conveys a great amount of information regarding structural and functional performance of the heart. Hence, ECG plays an essential role in the cardiac assessment, abnormality detection and clinical diagnosis. A clean ECG signal plays an imperative and vital role in the primary clinical analysis and diagnosis of cardiac diseases. Unfortunately, the greatest obstacle in analyzing and interpreting an ECG signal is the presence of unwanted artifacts and noises as they contaminate and degrade the quality of the ECG signals. As a result, removal of unwanted artifacts and noises from an ECG signal becomes an indispensable task to ensure an accurate and reliable ECG analysis could be performed. In this study, many ECG noise reduction and enhancement methods based on various digital filter designs, as well as discrete wavelet transform with various mother wavelets, are modelled to investigate and benchmark their performance in term of Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE). This testing are based on ten randomly selected ECG datasets acquired from ECG-ID Database (ecgiddb) which available in PhysioNet. Based on structured qualitative and quantitative performance analysis, results conclude that the discrete wavelet transform with db8 as mother wavelet outperforms the Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filter designs in de-noising and enhancing a raw ECG signal with highest SNR value of 4.4148, at the same time achieve significant lowest RMSE value of 4.0767. This is due to the reason that discrete wavelet transform method has advantages in analyzing the ECG signal in both time and frequency domain, thus causing less distortion to ECG signal.

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.

Constructing a Guided Filter by Exploiting the Butterworth Filter for ECG Signal Enhancement

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.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Sparse representation-based ECG signal enhancement and QRS detection

Electrocardiogram (ECG) signal enhancement and QRS complex detection is a critical preprocessing step for further heart disease analysis and diagnosis. In this paper, we propose a sparse representation-based ECG signal enhancement and QRS complex detection algorithm. Unlike traditional Fourier or wavelet transform-based methods, which use fixed bases, the proposed algorithm models the ECG signal as the superposition of a few inner structures plus additive random noise, where these structures (referred to here as atoms) can be learned from the input signal or a training set. Using these atoms and their properties, we can accurately approximate the original ECG signal and remove the noise and other artifacts such as baseline wandering. Additionally, some of the atoms with larger kurtosis values can be modified and used as an indication function to detect and locate the QRS complexes in the enhanced ECG signals. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art ECG enhancement and QRS detection algorithms using both simulated and real-life ECG recordings.

ECG signal enhancement based on improved denoising auto-encoder

The electrocardiogram (ECG) is a primary diagnostic tool for examining cardiac tissue and structures. ECG signals are often contaminated by noise, which can manifest with similar morphologies as an ECG waveform in the frequency domain. In this paper, a novel deep neural network (DNN) is proposed to solve the above mentioned problem. This DNN is created from an improved denoising auto-encoder (DAE) reformed by a wavelet transform (WT) method. A WT with scale-adaptive thresholding method is used to filter most of the noise. A DNN based on improved DAE is then used to remove any residual noise, which is often complex with an unknown distribution in the frequency domain. The proposed method was evaluated on ECG signals from the MIT-BIH Arrhythmia database, and added noise signals were obtained from the MIT-BIH Noise Stress Test database. The results show that the average of output signal-to-noise ratio (SNR) is from 21.56dB to 22.96dB, and the average of root mean square error (RMSE) is less than 0.037. The proposed method showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement.

FPGA Based Arrhythmia Detection

Abstract This paper proposes a simple and reliable Field Programmable Gate Array (FPGA) based ECG Analysis system. Electrocardiogram (ECG) is important biomedical signal, which show the electrical activity of the heart. ECG waveform provides the valuable information for detection of abnormal heart diseases. For accurate analysis, ECG signal must be processed to remove the noise signal. Also, various features of ECG must be extracted for diagnosis of cardiac disorders. Thus, ECG signal processing includes two stages: Preprocessing and Feature Extraction. Preprocessing stage removes noise from the raw ECG signal and the feature extraction stage extracts diagnostic information from the ECG signal. The main objectives of the work are (i) ECG signal enhancement using Empirical mode decomposition (EMD) based method. (ii) Detection of R peaks which is the first step towards automatic detection of cardiac arrhythmias in ECG signal. The proposed system can detect three different arrhythmias. 94.76% accuracy of the proposed method is achieved in detecting the different heart Arrhythmias correctly by using subset of data records from the MIT-BIH database . The system is implemented using Verilog HDL and Xilinx Spartan 3E FPGA respectively.

ECG signal enhancement using adaptive Kalman filter and signal averaging

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG signals are utilized by physicians for interpretation and identification of physiological and pathological phenomena. Several techniques have been proposed to extract the ECG components contaminated with the background noise and allow the measurement of subtle features in the ECG signal. One of the common approaches is the adaptive filter architecture, which has been used for the noise cancelation of ECGs containing baseline wander, electromyogram (EMG) noise, and motion artifacts [1].

ECG signal enhancement using S-Transform

Electrocardiogram (ECG), which is a noninvasive technique, is used generally as a primary diagnostic tool for cardiovascular diseases. In real-time scenario, noises like channel noise, muscle artifacts, electrode motion and baseline wander are often embedded with ECG signals during acquisition and transmission. In this paper, an automatic ECG signal enhancement technique is proposed to remove noise components from time–frequency domain represented noisy ECG signal. Stockwell transform (S-Transform) is used in this work to represent the noisy ECG signal in time–frequency domain. Next, masking and filtering technique is applied to remove unwanted noise components from time–frequency domain. The proposed technique does not require any prior information like R-peak position or reference signal as auxiliary signal. This method is evaluated on ECG signals which are available in MIT-BIH Arrhythmia database. The experimental results demonstrate that the proposed method shows better signal to noise ratio (SNR) and lower root means square error (RMSE) compared to earlier reported wavelet transform with soft thresholding (WT-Soft) and wavelet transform with subband dependent threshold (WT-Subband) based technique. To quantify the significant difference among all methods, the performances of different ECG enhancement techniques at 1.25dB input SNR level are compared using analysis of variance (ANOVA) based statistical evaluation technique and it is seen that the proposed method yields superior performance compared to other methods. R-peak detection test is also conducted on enhanced ECG signal in addition to SNR and RMSE to evaluate the quality of biology-related information preserved in the enhanced ECG signal. The performance of R-peak detection for denoised ECG signals, in terms of sensitivity and positive predictivity using proposed enhancement method, is also better than WT-Soft, WT-Subband methods, and validates the superiority of the proposed method.

Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering

Electrocardiogram (ECG) is required during magnetic resonance (MR) examination for monitoring patients under anaesthesia or with heart diseases and for synchronizing image acquisition with heart activity (triggering). Accurate and fast QRS detection is therefore desirable, but this task is complicated by artefacts related to the complex MR environment (high magnetic field, radio-frequency pulses and fast switching magnetic gradients). Specific signal processing has been proposed, whether using specific MR QRS detectors or ECG denoising methods. Most state-of-the-art techniques use a connection to the MR system for achieving their task, which is a major drawback since access to the MR system is often restricted. This paper introduces a new method for on-line ECG signal enhancement, called ICARE, which takes advantage of using multi-lead ECG and does not require any connection to the MR system. It is based on independent component analysis (ICA) and applied in real time. This algorithm yields accurate QRS detection for efficient triggering.