Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

Dubois, Rémi; Maison‐Blanche, Pierre; Quenet, Brigitte; Dreyfus, Gérard

doi:10.1016/j.cmpb.2007.09.005

Cited by 41 publications

(22 citation statements)

References 28 publications

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“…[28][29][30] Subsequently, the projection of the T wave on the PCA principal dimension is modeled by a fourparameter function called bi-Gaussian function estimated by minimization of the least-square error between the BGF model and the T-wave signal.…”

Section: Bgf Mathematical Modelmentioning

confidence: 99%

The Time Course of New T‐Wave ECG Descriptors Following Single‐ and Double‐Dose Administration of Sotalol in Healthy Subjects

Extramiana

Dubois

Vaglio³

et al. 2010

Noninvasive Electrocardiol

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Introduction:The aim of the study was to assess the time course effect of IKr blockade on ECG biomarkers of ventricular repolarization and to evaluate the accuracy of a fully automatic approach for QT duration evaluation.Methods: Twelve-lead digital ECG Holter was recorded in 38 healthy subjects (27 males, mean age = 27.4 ± 8.0 years) on baseline conditions (day 0) and after administration of 160 mg (day 1) and 320 mg (day 2) of d-l sotalol. For each 24-hour period and each subject, ECGs were extracted every 10 minutes during the 4-hour period following drug dosage. Ventricular repolarization was characterized using three biomarker categories: conventional ECG time intervals, principal component analysis (PCA) analysis on the T wave, and fully automatic biomarkers computed from a mathematical model of the T wave.Results: QT interval was significantly prolonged starting 1 hour 20 minutes after drug dosing with 160 mg and 1 hour 10 minutes after drug dosing with 320 mg. PCA ventricular repolarization parameters sotalol-induced changes were delayed (>3 hours). After sotalol dosing, the early phase of the T wave changed earlier than the late phase prolongation. Globally, the modeled surrogate QT paralleled manual QT changes.The duration of manual QT and automatic surrogate QT were strongly correlated (R 2 = 0.92, P < 0.001). The Bland and Altman plot revealed a nonstationary systematic bias (bias = 26.5 ms ± 1.96 * SD = 16 ms).Conclusions: Changes in different ECG biomarkers of ventricular repolarization display different kinetics after administration of a potent potassium channel blocker. These differences need to be taken into account when designing ventricular repolarization ECG studies.

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Section: Bgf Mathematical Modelmentioning

confidence: 99%

The Time Course of New T‐Wave ECG Descriptors Following Single‐ and Double‐Dose Administration of Sotalol in Healthy Subjects

Extramiana

Dubois

Vaglio³

et al. 2010

Noninvasive Electrocardiol

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“…Overall classification accuracy, Tc Sensitivity: true positive ratio TPR Specificity : true negative ratio TNR are calculated by using confusion matrix TNR=100%.TN/(TN+FP) (9) TPR=100%.TP/(TP+FN) The results in table 4 reflect the performance of the proposed classifier . We think that the performance of the method will be better if the number of the beats for the learning is increased.…”

Section: Classmentioning

confidence: 99%

“…This algorithm is used to find the five most relevant Gaussians which are selected from the library and adjusted with the beat model [9].…”

Section: N Number Of Gaussianmentioning

confidence: 99%

Neural networks and SVM for heartbeat classification

Kedir-Talha

Ould-Slimane

2012

2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA)

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The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heartbeats. The purpose of this work is to propose a diagnostic system for modelling and classification of heartbeat, by use of time features and Support vector machines (SVM) classification algorithm. Neural Networks learning allow us to select a features of each heart beat on the basis of Generalized Orthogonal Forward Regression (GOFR) algorithm and a library of 132 Gaussians with different standard deviations and different means, each beat is represented by five Gaussians with different amplitudes. The parameters of this system are determined and its performance is evaluated for the MIT-BIH arrhythmia database. For a database of 364 normal heartbeats and 1148 abnormal heartbeats, we apply the SVM algorithm with Radial Basis Function kernel. Our results demonstrate that the testing performance of the neural network and SVM diagnostic system is found to be very satisfactory with a recognition rate of 99.67%.

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“…The performance of arrhythmia classification is quantified using the widely accepted statistical metrics: accuracy (Ac), sensitivity (Se) and positive predictivity (+P) [19]. To evaluate the overall performance for all records, the weighted measure [20] is also used according to the number of beats of each state that exist in the record.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

A Real-Time Cardiac Arrhythmia Classification System with Wearable Electrocardiogram

Shao

Tan

2011

2011 International Conference on Body Sensor Networks

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Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. A design of a real-time wearable ECG monitoring system with cardiac arrhythmia classification is proposed in this paper. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Last, human activities by an accelerometer can be identified to reduce the chance of false alarm in classification due to the motion artifacts.

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Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

Cited by 41 publications

References 28 publications

The Time Course of New T‐Wave ECG Descriptors Following Single‐ and Double‐Dose Administration of Sotalol in Healthy Subjects

The Time Course of New T‐Wave ECG Descriptors Following Single‐ and Double‐Dose Administration of Sotalol in Healthy Subjects

Neural networks and SVM for heartbeat classification

A Real-Time Cardiac Arrhythmia Classification System with Wearable Electrocardiogram

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