Morphological descriptors based on Eigen value decomposition for P-wave analysis

2008 ◽  
Author(s):  
F. Castells ◽  
J. Lorenz ◽  
A.M. Climent ◽  
M.S. Guillem ◽  
D. Husser ◽  
...  
Keyword(s):  
P Wave ◽  
Wave Analysis ◽  
Morphological Descriptors ◽  
Eigen Value ◽  
Value Decomposition

244An automated beat exclusion algorithm to improve beat-to-beat P-wave morphology analysis

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Tachmatzidis ◽  
D Filos ◽  
I Chouvarda ◽  
A Tsarouchas ◽  
D Mouselimis ◽  
...  
Keyword(s):  
P Wave ◽  
Strongly Correlated ◽  
Wave Analysis ◽  
Morphology Analysis ◽  
Wave Morphology ◽  
History Of ◽  
Ectopic Beats ◽  
Rater Variability ◽  
Operator Workload

Abstract Background A manually beat-to-beat P-wave analysis has previously revealed the existence of multiple P-wave morphologies in patients with paroxysmal Atrial Fibrillation (AF) while on sinus rhythm, distinguishing them from healthy, AF free patients. Purpose The aim of this study was to investigate the effectiveness of an Automated Beat Exclusion algorithm (ABE) that excludes noisy or ectopic beats, replacing manual beat evaluation during beat-to-beat P-wave analysis, by assessing its effect on inter-rater variability and reproducibility. Methods Beat-to-beat P-wave morphology analysis was performed on 34 ten-minute ECG recordings of patients with a history of AF. Each recording was analyzed independently by two clinical experts for a total of four analysis runs; once with ABE and once again with the manual exclusion of ineligible beats. The inter-rater variability and reproducibility of the analysis with and without ABE were assessed by comparing the agreement of analysis runs with respect to secondary morphology detection, primary morphology ECG template and the percentage of both, as these aspects have been previously used to discriminate PAF patients from controls. Results Comparing ABE to manual exclusion in detecting secondary P-wave morphologies displayed substantial (Cohen"s k = 0.69) to almost perfect (k = 0.82) agreement. Area difference among auto and manually calculated main morphology templates was in every case <5% (p < 0.01) and the correlation coefficient was >0.99 (p < 0.01). Finally, the percentages of beats classified to the primary or secondary morphology per recording by each analysis were strongly correlated, for both main and secondary P-wave morphologies, ranging from ρ=0.756 to ρ=0.940 (picture) Conclusion The use of the ABE algorithm does not diminish inter-rater variability and reproducibility of the analysis. The primary and secondary P-wave morphologies produced by all analyses were similar, both in terms of their template and their frequency. Based on the results of this study, the ABE algorithm incorporated in the beat-to-beat P-wave morphology analysis drastically reduces operator workload without influencing the quality of the analysis. Abstract Figure.

scholarly journals Associations between Intrinsic Heart Rate, P Wave and QT Interval Durations and Pulse Wave Analysis in Patients with Hypertension and High Normal Blood Pressure

2020 ◽  
Vol 17 (12) ◽  
pp. 4350
Author(s):  
Ioana Mozos ◽  
Cristina Gug ◽  
Costin Mozos ◽  
Dana Stoian ◽  
Marius Pricop ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Qt Interval ◽  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
P Wave ◽  
Normal Blood Pressure ◽  
Wave Analysis ◽  
Wave Duration ◽  
P Wave Duration

The present study aimed to explore the relationship between electrocardiographic (ECG) and pulse wave analysis variables in patients with hypertension (HT) and high normal blood pressure (HNBP). A total of 56 consecutive, middle-aged hypertensive and HNBP patients underwent pulse wave analysis and standard 12-lead ECG. Pulse wave velocity (PWV), heart rate, intrinsic heart rate (IHR), P wave and QT interval durations were as follows: 7.26 ± 0.69 m/s, 69 ± 11 beats/minute, 91 ± 3 beats/minute, 105 ± 22 mm and 409 ± 64 mm, respectively. Significant correlations were obtained between PWV and IHR and P wave duration, respectively, between early vascular aging (EVA) and P wave and QT interval durations, respectively. Linear regression analysis revealed significant associations between ECG and pulse wave analysis variables but multiple regression analysis revealed only IHR as an independent predictor of PWV, even after adjusting for blood pressure variables and therapy. Receiver-operating characteristic (ROC) curve analysis revealed P wave duration (area under curve (AUC) = 0.731; 95% CI: 0.569–0.893) as a predictor of pathological PWV, and P wave and QT interval durations were found as sensitive and specific predictors of EVA. ECG provides information about PWV and EVA in patients with HT and HNBP. IHR and P wave durations are independent predictors of PWV, and P wave and QT interval may predict EVA.

scholarly journals Beat-to-Beat P-Wave Analysis Outperforms Conventional P-Wave Indices in Identifying Patients with a History of Paroxysmal Atrial Fibrillation during Sinus Rhythm

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1694
Author(s):  
Dimitrios Tachmatzidis ◽  
Dimitrios Filos ◽  
Ioanna Chouvarda ◽  
Anastasios Tsarouchas ◽  
Dimitrios Mouselimis ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Paroxysmal Atrial Fibrillation ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
P Wave ◽  
P Value ◽  
Wave Analysis ◽  
P Waves ◽  
History Of

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers—one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754–0.917) vs. SPWIs AUC: 0.721 (0.613–0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients.

scholarly journals Role of Spin in NN → NNπ

2016 ◽  
Vol 40 ◽  
pp. 1660061
Author(s):  
Vadim Baru
Keyword(s):  
Partial Wave ◽  
Pion Production ◽  
Low Energy ◽  
Partial Wave Analysis ◽  
P Wave ◽  
Wave Analysis ◽  
Theoretical Predictions

The recent measurements of the reactions [Formula: see text] and [Formula: see text] by the ANKE collaboration at COSY are analyzed with the focus on the p-wave pion production amplitudes. These amplitudes are known to provide an important connection between [Formula: see text] and other low-energy few-nucleon reactions. The results of the recent partial wave analysis of the ANKE data are discussed and compared with the theoretical predictions.

scholarly journals P1158Signal averaged P-wave analysis to predict successful radiofrequency catheter ablation in patients with atrial fibrillation

EP Europace ◽  
2018 ◽  
Vol 20 (suppl_1) ◽  
pp. i221-i221
Author(s):  
C La Greca ◽  
D Pecora ◽  
A Sorgato ◽  
U Simoncelli ◽  
C Cuccia
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Radiofrequency Catheter Ablation ◽  
P Wave ◽  
Wave Analysis

scholarly journals Bayés’ Syndrome—A Comprehensive Short Review

Medicina ◽  
2020 ◽  
Vol 56 (8) ◽  
pp. 410
Author(s):  
Emma Murariu ◽  
Attila Frigy
Keyword(s):  
Conduction Block ◽  
Short Review ◽  
P Wave ◽  
Clinical Implications ◽  
Wave Analysis ◽  
Primary And Secondary Prevention ◽  
Important Data ◽  
Increased Risk ◽  
Bachmann’S Bundle ◽  
Interatrial Conduction

Prediction and early detection of atrial fibrillation (AF) remain a permanent challenge in everyday practice. Timely identification of an increased risk for AF episodes (which are frequently asymptomatic) is essential in the primary and secondary prevention of cardioembolic events. One of the noninvasive modalities of AF prediction is represented by the electrocardiographic P-wave analysis. This includes the study and diagnosis of interatrial conduction block (Bachmann’s bundle block). Bayés’ Syndrome (named after its first descriptor) denotes the association between interatrial conduction defect and supraventricular arrhythmias (mainly AF) predisposing to cardioembolic events. Our short review presents an update of the most important data concerning this syndrome: brief history, main ECG features, pathophysiological background and clinical implications.

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