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

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.

Download Full-text

Beat-to-beat P-wave analysis outperforms conventional P-wave indices in identifying patients with a history of paroxysmal atrial fibrillation, during sinus rhythm

European Heart Journal ◽

10.1093/eurheartj/ehab724.0306 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

D Tachmatzidis ◽

D Filos ◽

A Tsarouchas ◽

D Mouselimis ◽

C Bakogiannis ◽

...

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Roc Curves ◽

P Wave ◽

Wave Analysis ◽

P Waves ◽

Receiver Operating Characteristic Curves ◽

Funding Sources ◽

History Of ◽

The One

Abstract Introduction Atrial fibrillation (AF) is the most common arrhythmia and is associated with high risk of morbidity and mortality. In many patients, AF is of episodic character (paroxysmal AF – PAF), which makes the identification of these patients during sinus rhythm (SR) challenging. Purpose The aim of the present study is to compare the performance of beat-to-beat P-wave analysis with P-wave indices used as predictors of PAF, such as P-wave duration, area, voltage, axis, terminal force in V1, inter-atrial block or orthogonal type, in identifying patients with history of PAF during sinus rhythm. Methods Standard 12-lead ECG and 10-minute orthogonal ECG recordings were obtained from 40 consecutive patients with short history of PAF under no antiarrhythmic medication and 60 age- and sex- matched healthy controls. The P-waves on the 10-minute recordings were analyzed on a beat-to-beat basis and classified as belonging to a primary or secondary morphology according to previous study. Wavelet transform used to further analyze P-wave orthogonal signals of main morphology on a beat-to-beat basis. Results 38 out of 327 studied features were found to differ significantly among the two groups. These features were tested for their diagnostic ability and receiver operating characteristic curves were ploted. Only 3 of them performed adequetly, with an area under curve (AUC) above 0.65; Two of them came from morphology analysis (percentage of beats following main morphology in axis X and Y) and one from wavelet analysis (max energy in high frequency zone -Y axis). Among standard P-wave indices, P-wave area in lead II was the one with the highest AUC (0.64). Conclusion Novel indices derived from beat-to-beat analysis outperform stadard P-wave markers in identifying patients with PAF history during sinus rhythm. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of most significant features AUC characteristics of P-wave indices

Download Full-text

Detection of hypertensive patients at risk for paroxysmal atrial fibrillation during sinus rhythm by computer-assisted P wave analysis

Journal of Hypertension ◽

10.1097/00004872-199917100-00015 ◽

1999 ◽

Vol 17 (10) ◽

pp. 1463-1470 ◽

Cited By ~ 54

Author(s):

Polychronis E. Dilaveris ◽

Elias J. Gialafos ◽

Dimitris Chrissos ◽

George K. Andrikopoulos ◽

Dimitris J. Richter ◽

...

Keyword(s):

Atrial Fibrillation ◽

At Risk ◽

Sinus Rhythm ◽

Paroxysmal Atrial Fibrillation ◽

P Wave ◽

Computer Assisted ◽

Wave Analysis ◽

Hypertensive Patients ◽

Patients At Risk

Download Full-text

Spatial Pattern of P Waves in Paroxysmal Atrial Fibrillation Patients in Sinus Rhythm and Controls

Pacing and Clinical Electrophysiology ◽

10.1111/j.1540-8159.2012.03428.x ◽

2012 ◽

Vol 35 (7) ◽

pp. 819-826 ◽

Cited By ~ 4

Author(s):

DANIELE GIACOPELLI ◽

JOHN P. BOURKE ◽

ALAN MURRAY ◽

PHILIP LANGLEY

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Spatial Pattern ◽

Paroxysmal Atrial Fibrillation ◽

P Waves

Download Full-text

The signal-averaged P-wave duration is longer in hypertensive patients with history of paroxysmal atrial fibrillation as compared to those without

International Journal of Cardiology ◽

10.1016/j.ijcard.2004.08.027 ◽

2005 ◽

Vol 103 (1) ◽

pp. 37-40 ◽

Cited By ~ 10

Author(s):

Kudret Aytemir ◽

Basri Amasyali ◽

Gulcan Abali ◽

Sedat Kose ◽

Ayhan Kilic ◽

...

Keyword(s):

Atrial Fibrillation ◽

Paroxysmal Atrial Fibrillation ◽

P Wave ◽

Hypertensive Patients ◽

Wave Duration ◽

P Wave Duration ◽

History Of

Download Full-text

P-049 high resolution P wave analysis in signal averaged electrocardiography identifies patients at risk of developing paroxysmal atrial fibrillation after coronary artery bypass

EP Europace ◽

10.1016/s1099-5129(03)91801-0 ◽

2003 ◽

Vol 4 ◽

pp. B78

Author(s):

C OMICHI

Keyword(s):

Atrial Fibrillation ◽

At Risk ◽

Coronary Artery ◽

High Resolution ◽

Coronary Artery Bypass ◽

Paroxysmal Atrial Fibrillation ◽

Artery Bypass ◽

P Wave ◽

Wave Analysis ◽

Patients At Risk

Download Full-text

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

EP Europace ◽

10.1093/europace/euaa162.164 ◽

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.

Download Full-text