scholarly journals Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 328 ◽  
Author(s):  
Giorgio ◽  
Rizzi ◽  
Guaragnella
Keyword(s):  
Signal To Noise Ratio ◽  
Academic Community ◽  
Ventricular Late Potentials ◽  
Ecg Signals ◽  
Efficient Detection ◽  
Late Potential ◽  
Cardiac Signals ◽  
Sudden Cardiac Death Risk ◽  
Sensitivity Specificity ◽  
Low Amplitude

The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

scholarly journals Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1000 ◽  
Author(s):  
Cataldo Guaragnella ◽  
Maria Rizzi ◽  
Agostino Giorgio
Keyword(s):  
Component Analysis ◽  
Late Potentials ◽  
Method Performance ◽  
Ventricular Late Potentials ◽  
Ecg Signals ◽  
Vector Decomposition ◽  
Arrhythmia Risk Stratification ◽  
Low Amplitude ◽  
Condition Diagnosis ◽  
Vector Magnitude

Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

Some characteristics of low-amplitude cardiac signals

2011 ◽  
Vol 54 (3) ◽  
pp. 210-217 ◽  
Author(s):  
G. M. Teptin ◽  
I. A. Latfullin ◽  
L. E. Mamedova ◽  
Z. F. Kim
Keyword(s):  
Cardiac Signals ◽  
Low Amplitude

Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

2020 ◽  
Vol 19 (03) ◽  
pp. 2050027
Author(s):  
Thandar Oo ◽  
Pornchai Phukpattaranont
Keyword(s):  
Signal To Noise Ratio ◽  
Training Data ◽  
Signal To Noise ◽  
Zero Crossing ◽  
Ecg Signals ◽  
Average Value ◽  
Emg Signal ◽  
Recognition Systems ◽  
Noise Ratio ◽  
Electromyography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

scholarly journals Sparse ECG Denoising with Generalized Minimax Concave Penalty

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1718 ◽  
Author(s):  
Zhongyi Jin ◽  
Anming Dong ◽  
Minglei Shu ◽  
Yinglong Wang
Keyword(s):  
Root Mean Square ◽  
Signal To Noise Ratio ◽  
High Amplitude ◽  
Mean Square ◽  
Ecg Signals ◽  
Reduction Techniques ◽  
Ecg Denoising ◽  
Important Diagnostic Tool ◽  
Sparsity Recovery ◽  
Norm Penalty

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

scholarly journals Nonlinearity and noise at the rod—rod bipolar cell synapse

2010 ◽  
Vol 28 (1) ◽  
pp. 61-68 ◽  
Author(s):  
E. BRADY TREXLER ◽  
ALEXANDER R.R. CASTI ◽  
YU ZHANG
Keyword(s):  
Quantum Efficiency ◽  
Single Photon ◽  
Signal To Noise Ratio ◽  
Discrete Events ◽  
Amplitude Noise ◽  
Synaptic Noise ◽  
Suction Electrode ◽  
Suppression Mechanism ◽  
Low Amplitude ◽  
Cell Synapse

AbstractIn the retina, rod bipolar (RBP) cells synapse with many rods, and suppression of rod outer segment and synaptic noise is necessary for their detection of rod single-photon responses (SPRs). Depending on the rods’ signal-to-noise ratio (SNR), the suppression mechanism will likely eliminate some SPRs as well, resulting in decreased quantum efficiency. We examined this synapse in rabbit, where 100 rods converge onto each RBP. Suction electrode recordings showed that rabbit rod SPRs were difficult to distinguish from noise (independent SNR estimates were 2.3 and 2.8). Nonlinear transmission from rods to RBPs improved response detection (SNR = 8.7), but a large portion of the rod SPRs was discarded. For the dimmest flashes, the loss approached 90%. Despite the high rejection ratio, noise of two distinct types was apparent in the RBP traces: low-amplitude rumblings and discrete events that resembled the SPR. The SPR-like event frequency suggests that they result from thermal isomerizations of rhodopsin, which occurred at the rate 0.033/s/rod. The presence of low-amplitude noise is explained by a sigmoidal input–output relationship at the rod—RBP synapse and the input of noisy rods. The rabbit rod SNR and RBP quantum efficiency are the lowest yet reported, suggesting that the quantum efficiency of the rod—RBP synapse may depend on the SNR in rods. These results point to the possibility that fewer photoisomerizations are discarded for species such as primate, which has a higher rod SNR.

scholarly journals Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yatao Zhang ◽  
Shoushui Wei ◽  
Yutao Long ◽  
Chengyu Liu
Keyword(s):  
Signal To Noise Ratio ◽  
Stress Test ◽  
Low Frequency ◽  
Scale Factor ◽  
Multiscale Entropy ◽  
Signal Quality ◽  
Ecg Signal ◽  
The Real ◽  
Ecg Signals ◽  
Test Database

This study explored the performance of multiscale entropy (MSE) for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF) noise, low frequency (LF) noise, and power-line (PL) noise, was analyzed. The sensitivity of MSE to the signal to noise ratio (SNR) of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could reflect content level of various noises contained in the ECG signals. For the synthetic ECG plus LF noise, the MSE was sensitive to SNR within higher range of scale factor. However, for the synthetic ECG plus HF noise, the MSE was sensitive to SNR within lower range of scale factor. Thus, a recommended scale factor range within 5 to 10 was given. Finally, the results were verified on the real ECG signals, which were derived from MIT-BIH Arrhythmia Database and Noise Stress Test Database. In all, MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.

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