Atrial repolarization as observable during the PQ interval

Ihara, Z.; Oosterom, A. van; Hoekema, R.

doi:10.1016/j.jelectrocard.2005.12.001

Cited by 37 publications

(34 citation statements)

References 20 publications

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“…The second approach instead proposes to combine the individual leads into a multilead signal-which provides an overall view of the cardiac phenomena and is independent of the lead system used-on which a single-lead delineation algorithm is further applied. Different multilead signals can be found in the literature: 1) the combination of the ECG leads in the root mean squared (RMS) sense [17]- [19]; 2) a derived spatial lead obtained via a multistep iterative search on the spatial WT loops of the two or three original orthogonal leads [20]; 3) a combination of the derivatives of the ECG leads [21]. Given the limited processing and storage resources of our target embedded sensor node, we herein retain the wellestablished RMS-based multilead delineation approach due to its lower computational complexity and better scalability.…”

Section: B Multilead Delineationmentioning

confidence: 99%

“…For a meaningful combination, it is crucial to remove baseline wander (mainly caused by respiration, electrode impedance changes due to perspiration and body movements) on each of the leads before computing the RMS [17], [18]. Therefore, since the quality of the subsequent delineation depends on the baseline wander correction, we assess in this work the effectiveness of the following two state-ofthe-art approaches.…”

Section: B Multilead Delineationmentioning

confidence: 99%

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Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

Rincon

Recas

Khaled

et al. 2011

IEEE Trans. Inform. Technol. Biomed.

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Abstract-This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG)wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems.Index Terms-Ambulatory electrocardiogram, delineation, digital wavelet transform, energy-constrained systems, multilead, wireless sensor node.

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Section: B Multilead Delineationmentioning

confidence: 99%

Section: B Multilead Delineationmentioning

confidence: 99%

Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

Rincon

Recas

Khaled

et al. 2011

IEEE Trans. Inform. Technol. Biomed.

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“…The point of P wave onset was chosen for the cubic spline interpolation since it is the most quiet? and stable point in the whole electrocardiogram [13].…”

Section: Data Processingmentioning

confidence: 98%

ECG based prediction of Atrial Fibrillation using Support Vector Classifier

2011

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Original scientific paperIn patients undergoing Coronary Artery Bypass Grafting (CABG) surgery postoperative atrial fibrillation (AF) occurs with prevalence of up to 40%. The highest incidence is between the second and third day after the operation. Following cardiac surgery AF causes various complications, hemodynamic instability, and can cause heart attack, cerebral and other thromboemolisms. AF increases morbidity, duration and expense of medical treatment. This study aims to identify patients at high risk of postoperative AF. An early prediction of AF would provide a timely prophylactic treatment and would reduce incidence of arrhythmia. Patients at low risk of postoperative AF could be excluded from the side effects of anti-arrhythmic drugs. The investigation included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 hours following CABG. Univariate statistical analysis was used in the search of signal features that might predict AF. The most promising identified features were: P wave duration, RR interval duration and PQ segment level. On the basis of these a nonlinear multivariate prediction model was made deploying a Support Vector Machine (SVM) classifier. The prediction accuracy was found uprising over the time. At 48 hours following CABG; the measured best average sensitivity was 95.9% and specificity 93.4%. The positive and negative predictive accuracy were 88.9% and 98.8%, respectively and the overall accuracy was 94.6%. In regard to the prediction accuracy, the risk assessment and prediction of postoperative AF are optimal to be done in the period between 24 and 48 hours following CABG.Key words: Post-operative atrial fibrillation, Coronary artery bypass grafting (CABG), Cubic spline baseline correction, PQ segment levelPredvianje postoperacijske fibrilacije atrija korištenjem SVM klasifikatora. Postoperacijska fibrilacija atrija (AF) pojavljuje se u oko 40% pacijenata podvrgnutih operaciji aortokoronarnog premoštenja (CABG), s najvećom učestalosti pojavljivanja oko trećeg dana nakon operacije. Postoperacijska AF može stvoriti brojne komplikacije poput hemodinamske nestabilnosti, srčanog udara, cerebralnih i drugih tromboembolija; povećava morbiditet, trajanje i troškove liječenja. Studija ima za cilj rano otkrivanje pacijenta sa visokim rizikom razvoja postoperacijske AF, što bi osiguralo pravovremenu profilaktičku terapiju i smanjilo učestalost aritmije, dok bi pacijenti sa niskim rizikom razvoja postoperacijske AF bili pošteeni nuspojava antiaritmičkih lijekova. Podatkovni skup uključuje 50 pacijenata, snimanih II standardnim odvodom elektrokardiografa, kontinuirano u razdoblju od 48 sati nakon operacije. Univarijatna statistička analiza korištena je za odreivanje parametara signala koji bi mogli predvidjeti AF, te su kao najznačajniji odreeni: trajanje P vala, trajanje RR intervala i razina PQ spojnice; na temelju kojih je izveden nelinearni multivarijatni predikcijski model zasnovan na SVM klasifikatoru. Ukupna predikcijska točnost modela povećava se s vremenom. ...

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“…A derivative-based method is applied at the beginning of the algorithm to detect ventricular complexes [5], then at each step the initial VA dictionary is computed using patches taken from the estimated VA at these positions.…”

Section: Initializationmentioning

confidence: 99%

Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

Mailhé

Gribonval

Bimbot

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation approaches. Experiments on synthetic data show that this method can almost completely suppress the ventricular activity, but it generates some artifacts. Contrary to other ventricular cancellations methods, our approach also learns a model for the atrial activity.

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Atrial repolarization as observable during the PQ interval

Cited by 37 publications

References 20 publications

Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

ECG based prediction of Atrial Fibrillation using Support Vector Classifier

Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

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