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.

Time-Frequency Analysis of Ventricular Late Potentials

1994 ◽  
Vol 33 (02) ◽  
pp. 187-195 ◽  
Author(s):  
L. Khadra ◽  
J. Brachmann ◽  
H. Dickhaus
Keyword(s):  
Wavelet Transforms ◽  
Healthy Subjects ◽  
Simulated Data ◽  
Detection Accuracy ◽  
Late Potentials ◽  
Ventricular Late Potentials ◽  
Time Frequency ◽  
Ecg Signals ◽  
Healthy Control ◽  
Different Characteristics

Abstract:The time-frequency characteristics are studied of averaged and filtered ECG records from 21 patients with sustained ventricular tachycardia and 29 healthy control subjects. Simulated data as well as real ECG records reveal the detection accuracy of the wavelet transform of signals with late potentials. The wavelet-transforms of preprocessed ECG signals are plotted in the time-frequency plane. These representations of the signals are well suited to describe the different characteristics of the patients and healthy subjects. A quantitative discrimination was performed with a sensitivity of 90% and a specificity of 72% by the energy underneath the squared modulus of the time-frequency distribution plots of the computed wavelet transforms.

scholarly journals Investigation of Inaccuracies in the Assessment of Ventricular Late Potentials Using 12-lead ECG Recordings

2021 ◽  
Vol 24 (4) ◽  
pp. 79-89
Author(s):  
Solieman Hanadi ◽  
Trong Tuyen Nguyen
Keyword(s):  
Heart Rhythm ◽  
Late Potentials ◽  
Ventricular Late Potentials ◽  
Ecg Signals ◽  
Number Of Factors ◽  
Incorrect Prediction ◽  
Patient State ◽  
Characteristic Points ◽  
Cardiac Disorders ◽  
Ventricular Depolarization

Introduction. Ventricular late potentials (VLP) are predictors of cardiac disorders such as sudden death syndrome, myocardial infarction and ventricular tachyarrhythmias. Therefore, VLP assessment allows the severity and possible dangerous consequences of such disorders to be predicted.Aim. To determine errors associated with VLP assessment by high-resolution 12-lead ECG recordings.Materials and methods. VLPs were determined by the modulus of the cardiac electrical vector using signals from orthogonal leads. The conversion error was assessed using synchronous ECG recordings of 12-channel and orthogonal leads, the method of digital filtering (to reduce noise and interference) and the method of identifying characteristic points of the QRS complex and VLPs.Results. The conversion of 12-lead ECG signals into orthogonal signals results in errors associated with the assessment of both the modulus of the cardiac electrical vector and all VLP indicators. The Kors transformation was shown to provide the minimum errors when assessing the cardiac electrical vector modulus in the QRS area, with the errors related to the VRMS assessment not exceeding 0.084 %. The estimation of the QRSd and LAS errors should consider the nature of VLP variations and the zone of uncertainty in their assessment. The ambiguity of the results of assessing the boundaries of violations and the absence of pathologies in cardiac ventricular depolarization indicates the influence of a large number of factors on research accuracy. Errors in the assessment of these factors may result in under- and overestimation of dangerous heart rhythm disturbances and incorrect prediction of the patient' state.Conclusion. The obtained results can be used for reducing errors associated with the assessment of VLP indicators, improving the diagnostic accuracy of dangerous heart rhythm disturbances and predicting disease exacerbation due to structural and morphological disorders of the myocardium.

scholarly journals Time-domain signal-averaged electrocardiogram in healthy German Shepherd and Boxer dogs

2014 ◽  
Vol 66 (3) ◽  
pp. 778-786
Author(s):  
P.P.C. Chamas ◽  
V.M.C. Oliveira ◽  
F.L. Yamaki ◽  
M.H.M.A. Larsson
Keyword(s):  
Ventricular Tachycardia ◽  
Sudden Death ◽  
Cardiac Conduction ◽  
Late Potentials ◽  
Arrhythmogenic Cardiomyopathy ◽  
German Shepherd ◽  
Ventricular Late Potentials ◽  
Time Domain Signal ◽  
Low Amplitude ◽  
Boxer Dogs

Signal-averaged electrocardiogram (SAECG) identifies ventricular late potentials (LP), low-amplitude electrical signals that are markers of slow cardiac conduction in fibrous myocardium, consisting in a predictive factor for sudden death in dogs at risk of sustained ventricular tachycardia. The aim of this study was to establish reference values of SAECG for German Shepherd and Boxer dogs. SAECG was performed in 19 German Shepherd and 28 Boxer client-owned dogs, and parameters analyzed were QRSd (duration of filtered QRS), LAS<40μV (duration of low-amplitude signals in terminal portion of filtered QRS) and RMS40 (root square of mean voltage over the last 40 milliseconds of filtered QRS), with two different filters (25-250 Hz and 40-250 Hz). Statistical analyses was achieved by T Student test (p<0.05) to identify differences between the two groups and between the values obtained with the two filters. No statistical difference was found in SAECG variables between the two breeds with the two different filters (p>0.05). Achieving normal values of SAECG in German Shepherd and Boxer dogs is important to further research late potentials in animals of these breeds with hereditary ventricular tachycardia or arrhythmogenic cardiomyopathy and identification of individuals at high risk of cardiac-related sudden death.

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.

Sign in / Sign up

Export Citation Format

Share Document