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ECG Signals (ecg + signal)

Distribution by Scientific Domains

Medical Sciences	71%

Selected Abstracts

Usefulness of Nonlinear Analysis of ECG Signals for Prediction of Inducibility of Sustained Ventricular Tachycardia by Programmed Ventricular Stimulation in Patients with Complex Spontaneous Ventricular Arrhythmias

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 3 2008
Ornella Durin M.D.
Introduction: The aim of our study was to assess the effectiveness of the nonlinear analysis (NLA) of ECG in predicting the results of invasive electrophysiologic study (EPS) in patients with ventricular arrhythmias. Methods: We evaluated 25 patients with history of cardiac arrest, syncope, sustained, or nonsustained ventricular tachycardia (VT). All patients underwent electrophysiologic study (EPS) and nonlinear analysis (NLA) of ECG. The study group was compared with a control group of 25 healthy subjects, in order to define the normal range of NLA. ECG was processed in order to obtain numerical values, which were analyzed by nonlinear mathematical functions. Patients were classified through the application of a clustering procedure to the whole set of functions, and the correlation between the results of nonlinear analysis of ECG and EPS was tested. Results: NLA assigned all patients with negative EPS to the same class of healthy subjects, whereas the patients in whom VT was inducible had been correctly and clearly isolated into a separate cluster. In our study, the result of NLA with application of the clustering technique was significantly correlated to that of EPS (P < 0.001), and was able to predict the result of EPS, with a negative predictive value of 100% and a positive predictive value of 100%. Conclusions: NLA can predict the results of EPS with good negative and positive predictive value. However, further studies are needed in order to verify the usefulness of this noninvasive tool for sudden death risk stratification in patients with ventricular arrhythmias. [source]

Automated QT Analysis That Learns from Cardiologist Annotations

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
Iain Guy David Strachan Ph.D.
Background: Reliable, automated QT analysis would allow the use of all the ECG data recorded during continuous Holter monitoring, rather than just intermittent 10-second ECGs. Methods: BioQT is an automated ECG analysis system based on a Hidden Markov Model, which is trained to segment ECG signals using a database of thousands of annotated waveforms. Each sample of the ECG signal is encoded by its wavelet transform coefficients. BioQT also produces a confidence measure which can be used to identify unreliable segmentations. The automatic generation of templates based on shape descriptors allows an entire 24 hours of QT data to be rapidly reviewed by a human expert, after which the template annotations can automatically be applied to all beats in the recording. Results: The BioQT software has been used to show that drug-related perturbation of the T wave is greater in subjects receiving sotalol than in those receiving moxifloxacin. Chronological dissociation of T-wave morphology changes from the QT prolonging effect of the drug was observed with sotalol. In a definitive QT study, the percentage increase of standard deviation of QTc for the standard manual method with respect to that obtained with BioQT analysis was shown to be 44% and 30% for the placebo and moxifloxacin treatments, respectively. Conclusions: BioQT provides fully automated analysis, with confidence values for self-checking, on very large data sets such as Holter recordings. Automatic templating and expert reannotation of a small number of templates lead to a reduction in the sample size requirements for definitive QT studies. [source]

Computer-Based Analysis of Dynamic QT Changes: Toward High Precision and Individual Rate Correction

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 4 2002
Corina Dota M.D.
Background: New strategies are needed to improve the results of automatic measurement of the various parts of the ECG signal and their dynamic changes. Methods: The EClysis software processes digitally-recorded ECGs from up to 12 leads at 500 Hz, using strictly defined algorithms to detect the PQRSTU points and to measure ECG intervals and amplitudes. Calculations are made on the averaged curve of each sampling period (beat group) or as means ± SD for beat groups, after being analyzed at the individual beat level in each lead. Resulting data sets can be exported for further statistical analyses. Using QT and R-R measured on beat level, an individual correction for the R-R dependence can be performed. Results: EClysis assigns PQRSTU points and intervals in a sensitive and highly reproducible manner, with coefficients of variation in ECG intervals corresponding to ca. 2 ms in the simulated ECG. In the normal ECG, the CVs are 2% for QRS, 0.8% for QT, and almost 6% for PQ intervals. EClysis highlights the increase in QT intervals and the decrease of T-wave amplitudes during almokalant infusion versus placebo. Using the observed linear or exponential relationships to adjust QT for R-R dependence in healthy subjects, one can eliminate this dependence almost completely by individualized correction. Conclusions: The EClysis system provides a precise and reproducible method to analyze ECGs. A.N.E. 2002;7(4):289,301 [source]

Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines

EXPERT SYSTEMS, Issue 3 2009
Elif Derya Übeyli
Abstract: In the present study, the diagnostic accuracy of support vector machines (SVMs) on electrocardiogram (ECG) signals is evaluated. Two types of ECG beats (normal and partial epilepsy) were obtained from the Physiobank database. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVM trained on the extracted features. The present research demonstrates that the power levels of the power spectral densities obtained by eigenvector methods are features which represent the ECG signals well and SVMs trained on these features achieve high classification accuracies. [source]

Detection of electrocardiogram beats using a fuzzy similarity index

EXPERT SYSTEMS, Issue 2 2007
Elif Derya Übeyli
Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time,frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat). [source]

Electrocardiographic Activity before Onset of Postoperative Atrial Fibrillation in Cardiac Surgery Patients

PACING AND CLINICAL ELECTROPHYSIOLOGY, Issue 11 2008
MIRELA OVREIU Ph.D.
Background:Electrocardiographic (ECG) characteristics were analyzed in postoperative cardiac surgery patients in an attempt to predict development of new-onset postoperative atrial fibrillation (AF). Methods:Nineteen ECG characteristics were analyzed using computer-based algorithms. The parameters were retrospectively analyzed from ECG signals recorded in postoperative cardiac surgery patients while they were in the cardiovascular intensive care unit (CVICU) at our institution. ECG data from 99 patients (of whom 43 developed postoperative AF) were analyzed. A bootstrap variable selection procedure was applied to select the most important ECG parameters, and a multivariable logistic regression model was developed to classify patients who did and did not develop AF. Results:Premature atrial activity (PAC) was greater in AF patients (P < 0.01). Certain heart rate variability (HRV) and turbulence parameters also differed in patients who did and did not develop AF. In contrast, P-wave morphology was similar in patients with and without AF. Receiver operating curve (ROC) analysis applied to the model produced a C-statistic of 0.904. The model thus correctly classified AF patients with more than a 90% sensitivity and a 70% specificity. Conclusion:Among the 19 ECG parameters analyzed, PAC activity, frequency-domain HRV, and heart rate turbulence parameters were the best discriminators for postoperative AF. [source]