P-wave beat-to-beat morphology analysis outperforms conventional P-wave indices in detecting patients with paroxysmal atrial fibrillation

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
D Tachmatzidis ◽  
D Filos ◽  
A Tsarouchas ◽  
D Mouselimis ◽  
A Antoniadis ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Morphological Variability ◽  
Three Dimensional ◽  
Critical Issue ◽  
P Wave ◽  
P Waves ◽  
Morphology Analysis ◽  
Increased Risk

Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders. Purpose The aim of this study is to compare the performance of standard P-wave indices with beat-to-beat P-wave morphological variability parameters in identifying patients with history of Paroxysmal Atrial Fibrillation (PAF). Methods Three-dimensional 1000Hz ECG digital recordings of 10 minutes duration were obtained from a total of 39 PAF patients and 60 healthy individuals. Following artifacts and ectopic beats removal, P‑wave morphology analysis was performed based on the dynamic application of the k‑means clustering process and main and secondary P-wave morphologies were identified. The percentage of P-waves following the main or the secondary morphology in each lead was calculated, as well as established indices such as Advanced Interatrial Block, P-wave duration, axis and area, P-wave Terminal Force in lead V1 and Orthogonal Leads Type 1, 2 or 3. Results 9 out of 24 parameters studied, were found to be significantly different between the two groups. 7 of these indices were derived from morphology analysis and 2 from P-wave area. Logistic regression revealed that the percentage of P-waves allocated to main morphology in X axis performed better than all other indices in identifying patients with PAF history from healthy volunteers in terms of total accuracy and F1 measure. Conclusion P-wave beat-to-beat morphology analysis can identify PAF patients during normal sinus rhythm more efficiently than standard P-wave indices. Abstract Figure.

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.

219A machine learning classification algorithm to detect patients with paroxysmal atrial fibrillation during sinus rhythm

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Tachmatzidis ◽  
D Filos ◽  
I Chouvarda ◽  
D Mouselimis ◽  
A Tsarouchas ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
P Wave ◽  
Support Vector ◽  
Svm Classifier ◽  
P Waves ◽  
Machine Learning Classification ◽  
Increased Risk ◽  
Wave Morphology ◽  
The Mean

Abstract Background Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders. Purpose The aim of this study is to identify P-wave morphology patterns encountered in patients with Paroxysmal AF (PAF) and to develop a classifier discriminating PAF patients from healthy volunteers. Methods Three-dimensional 1000Hz ECG signals of 5 minutes duration were obtained through the use of a Galix GBI-3S Holter monitor from a total of 68 PAF patients and 52 healthy individuals. Signal pre-processing, consisting of denoising, QRS auto-detection, and ectopic beats removal was performed and a signal window of 250ms prior to the Q-wave (Pseg) was considered for every single beat. P‑wave morphology analysis based on the dynamic application of the k‑means clustering process was performed. For those Pseg that were assigned in the largest cluster, the mean P-wave was computed. The correlation of every P-wave with the mean P-wave of the main cluster was calculated. In case that it exceeded a prespecified threshold, the P-wave was allocated to the main morphology. For the remaining P‑waves, the same approach was followed once again, and the secondary morphology was extracted (picture). The P-waves of the dominant morphology were further analyzed using wavelet transform, whereas time-domain characteristics were also extracted. A Support Vector Machine (SVM) model was created using the Gaussian Radial Basis Function kernel and the forward feature selection wrapper approach was followed. ECGs were allocated to the training, internal validation, and testing datasets in a 3:1:1 ratio. Results The percentage of P-waves following the main morphology in all three leads was lower in PAF patients (91.2 ±7.3%) than in healthy subjects (96.1 ±3.5%, p = 0.02). Classification between the two groups highlighted 7 features, while the SVM classifier resulted in a balanced accuracy of 91.4 ± 0.2% (sensitivity 94.2 ± 0.3%, specificity 88.6 ± 0.1%) Conclusion An Artificial Intelligence based ECG Classifier can efficiently identify PAF patients during normal sinus rhythm. Abstract Figure.

scholarly journals Beat-to-beat P-wave morphological variability in patients with paroxysmal atrial fibrillation: anin silicostudy

EP Europace ◽  
2018 ◽  
Vol 20 (suppl_3) ◽  
pp. iii26-iii35 ◽  
Author(s):  
Simone Pezzuto ◽  
Ali Gharaviri ◽  
Ulrich Schotten ◽  
Mark Potse ◽  
Giulio Conte ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Morphological Variability ◽  
P Wave

scholarly journals Is there a change in P wave parameters following pulmonary vein isolation (PVI) using radiofrequency ablation (RF) vs. cryoballoon ablation (CRYO) for paroxysmal atrial fibrillation (PAF)?

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
I Antoun ◽  
S Bharat ◽  
A Mavilakandy ◽  
V Pooranachandran ◽  
GA Ng
Keyword(s):  
Atrial Fibrillation ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Paroxysmal Atrial Fibrillation ◽  
P Wave ◽  
Cryoballoon Ablation ◽  
Single Shot ◽  
P Waves ◽  
Significant Difference ◽  
Before And After

Abstract Funding Acknowledgements Type of funding sources: None. Pulmonary vein isolation (PVI) is an effective and established therapy for paroxysmal atrial fibrillation (PAF) . PVI can be performed using point by point ablation with radiofrequency (RF) or with single shot techniques such as cryoballoon (CRYO). As P waves represent atrial depolarization, we aimed to study whether P wave metrics may change after PVI and if there are differences between RF and CRYO approaches. Methods We studied 88 matched patients who had PVI for PAF between January 2017 and December 2018 (RF = 44, CRYO = 44). Each patient was in sinus rhythm (SR) prior to ablation. Patients on amiodarone and those who had additional linear ablation were excluded. Patients had continuous ECG monitoring using Labsystem Pro (Boston Scientific Inc). Sampled at 1kHz during the procedure. One-minute recordings before and after PVI were exported and analysed using custom-written software using MatLab (v2018, bandpass 1-50Hz) to annotate P wave onset, peak and end. P wave duration was heart rate corrected (PWDc) by using the Hodges formula and P wave amplitude (PWA). Results P wave metrics were comparable before PVI between both cohorts. Successful PVI was achieved in all patients. There was a trend towards an increase in PWDc in some ECG leads with either RF or CRYO but no significant difference in P wave metrics as a result of PVI ablation or between both ablation modalities. Conclusion In this study, there was no significant change seen in PVI with RF or CRYO and no difference between the 2 ablation modalities. P wave metrics comparison, RF vs CRYO PWDc (ms) PRE, RF (n = 44) POST, RF (n = 44) P PRE, CRYO (n = 44) POST CRYO (n = 44) P P (RF vs CRYO) I 134.7 ± 32 133.5 ± 35 0.813 131.9 ± 36 132.7 ± 39 0.9 0.81 II 140.9 ± 34 144.1 ± 37 0.56 139.4 ± 42 134.4 ± 40 0.51 0.41 III 131.5 ± 31 143.3 ± 37 0.04 132.8 ± 41 130.6 ± 36 0.68 0.074 AVF 137 ± 32 144.7 ± 36 0.15 137.5 ± 42 127.4 ± 37 0.11 0.141 V1 143.9 ± 33 151.8 ± 37 0.17 133.6 ± 37 143.8 ± 38 0.09 0.745 PWA (mV) PRE, RF (n = 44) POST, RF (n = 44) P PRE, CRYO (n = 44) POST CRYO (n = 44) P P (RF vs CRYO) I 0.125 ± 0.08 0.09 ± 0.06 0.002 0.13 ± 0.08 0.14 ± 0.09 0.59 0.076 II 0.238 ± 0.1 0.238 ± 0.1 0.98 0.232 ± 0.1 0.278 ± 0.2 0.1 0.212 III 0.149 ± 0.1 0.153 ± 0.1 0.83 0.189 ± 0.1 0.187 ± 0.1 0.97 0.86 AVF 0.195 ± 0.1 0.196 ± 0.1 0.92 0.197 ± 0.1 0.247 ± 0.1 0.066 0.132 V1 0.122 ± 0.1 0.151± 0.1 0.05 0.138 ± 0.1 0.193 ± 0.2 0.002 0.543 PWDc and PWA comparison following RF vs CRYO.

P Wave Dispersion in Hypertensive Patients with Paroxysmal Atrial Fibrillation

2000 ◽  
Vol 23 (11P2) ◽  
pp. 1859-1862 ◽  
Author(s):  
NECLA ÖZER ◽  
KUDRET AYTEMIR ◽  
ENVER ATALAR ◽  
ELIF SADE ◽  
SERDAR AKSÖYEK ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Wave Dispersion ◽  
P Wave ◽  
P Wave Dispersion ◽  
Hypertensive Patients

Predictors of recurrence after durable pulmonary vein isolation for paroxysmal atrial fibrillation

EP Europace ◽  
2020 ◽  
Author(s):  
Michelle Lycke ◽  
Maria Kyriakopoulou ◽  
Milad El Haddad ◽  
Jean-Yves Wielandts ◽  
Gabriela Hilfiker ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Paroxysmal Atrial Fibrillation ◽  
Prospective Trial ◽  
High Rate ◽  
Binary Logistic Regression Analysis ◽  
Ablation Time ◽  
Increased Risk ◽  
Af Recurrence

Abstract Aims Catheter ablation of paroxysmal atrial fibrillation (AF) reduces AF recurrence, AF burden, and improves quality of life. Data on clinical and procedural predictors of arrhythmia recurrence are scarce and are flawed by the high rate of pulmonary vein reconnection evidenced during repeat procedures after pulmonary vein isolation (PVI). In this study, we identified clinical and procedural predictors for AF recurrence 1 year after CLOSE-guided PVI, as this strategy has been associated with an increased PVI durability. Methods and results Patients with paroxysmal AF, who received CLOSE-guided PVI and who participated in a prospective trial in our centre, were included in this study. Uni- and multivariate models were plotted to find clinical and procedural predictors for AF recurrence within 1 year. Three hundred twenty-five patients with a mean age of 63 years (CHA2DS2VASc 1 [1–3], left atrium diameter 41 ± 6 mm) were included. About 60.9% were male individuals. After 1 year, AF recurrence occurred in 10.5% of patients. In a binary logistic regression analysis, the diagnosis-to-ablation time (DAT) was found to be the strongest predictor of AF recurrence (P = 0.011). Diagnosis-to-ablation time ≥1 year was associated with a nearly two-fold increased risk for developing AF recurrence. Conclusion The DAT is the most important predictor of arrhythmia recurrence in low-risk patients treated with durable pulmonary vein isolation for paroxysmal AF. Whether reducing the DAT could improve long-term outcomes should be investigated in another trial.

Sign in / Sign up

Export Citation Format

Share Document