Can orthogonal lead indicators of propensity to atrial fibrillation be accurately assessed from the 12-lead ECG?

Abstract Aims When analyzing P-wave morphology, the vectorcardiogram (VCG) has been shown useful to identify indicators of propensity to atrial fibrillation (AF). Since VCG is rarely used in the clinical routine, we wanted to investigate if these indicators could be accurately determined in VCG derived from standard 12-lead ECG (dVCG). Methods ECG and VCG recordings from 21 healthy subjects and 20 patients with a history of AF were studied. dVCG was calculated from ECG using the inverse Dower transform. Following signal averaging of P-waves, comparisons were made between VCG and dVCG, where three parameters characterizing signal shape and 15 parameters describing the P-wave morphology were used to assess the compatibility of the two recording techniques. The latter parameters were also used to compare the healthy and the AF groups. Results After transformation, P-wave shape was convincingly preserved. P-wave morphology parameters were consistent within the respective groups when comparing VCG and dVCG, with better preservation observed in the healthy group. Conclusion VCG derived from routine 12-lead ECG may be a useful alternate method for studying orthogonal P-wave morphology.

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On the Problem of Detecting Low Burden Atrial Fibrillation in Healthy Athletes: Comments on: „Abnormal P wave Morphology in Electrocardiogram in a Hurdle Runner with a History of Presyncope - A Case Report”

Medicina Sportiva ◽

10.2478/v10036-008-0010-1 ◽

2008 ◽

Vol 12 (2) ◽

pp. 49-50 ◽

Cited By ~ 1

Author(s):

Don R. Swanson

Keyword(s):

Atrial Fibrillation ◽

Case Report ◽

P Wave ◽

Wave Morphology ◽

History Of

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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 ◽

10.3390/diagnostics11091694 ◽

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.

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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.

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Data mining analysis of the influences of electrocardiogram P-wave morphology parameters on atrial fibrillation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189612 ◽

2021 ◽

pp. 1-8

Author(s):

Chen-Sen Ouyang ◽

Yenming J. Chen ◽

Jinn-Tsong Tsai ◽

Yiu-Jen Chang ◽

Tian-Hsiang Huang ◽

...

Keyword(s):

Atrial Fibrillation ◽

Heart Rate ◽

Cardiac Disease ◽

Parameter Extraction ◽

Extraction Methods ◽

P Wave ◽

Increased Risk ◽

Wave Morphology ◽

Ecg Database ◽

Morphology Parameters

Atrial fibrillation (AF) is a type of paroxysmal cardiac disease that presents no obvious symptoms during onset, and even the electrocardiograms (ECG) results of patients with AF appear normal under a premorbid status, rendering AF difficult to detect and diagnose. However, it can result in deterioration and increased risk of stroke if not detected and treated early. This study used the ECG database provided by the Physionet website (https://physionet.org), filtered data, and employed parameter-extraction methods to identify parameters that signify ECG features. A total of 31 parameters were obtained, consisting of P-wave morphology parameters and heart rate variability parameters, and the data were further examined by implementing a decision tree, of which the topmost node indicated a significant causal relationship. The experiment results verified that the P-wave morphology parameters significantly affected the ECG results of patients with AF.

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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

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Association of Atrial Fibrillation with Morphological and Electrophysiological Changes of the Atrial Myocardium

Acta Medica (Hradec Kralove Czech Republic) ◽

10.14712/18059694.2016.88 ◽

2016 ◽

Vol 59 (2) ◽

pp. 43-49 ◽

Cited By ~ 1

Author(s):

Adéla Matějková ◽

Ivo Šteiner

Keyword(s):

Atrial Fibrillation ◽

Structural Changes ◽

Anterior Wall ◽

Atrial Septum ◽

P Wave ◽

Atrial Myocardium ◽

P Waves ◽

Long Time ◽

History Of ◽

Right Atria

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. For long time it was considered as pure functional disorder, but in recent years, there were identified atrial locations, which are involved in the initiation and maintenance of this arrhythmia. These structural changes, so called remodelation, start at electric level and later they affect contractility and morphology. In this study we attempted to find a possible relation between morphological (scarring, amyloidosis, left atrial (LA) enlargement) and electrophysiological (ECG features) changes in patients with AF. We examined grossly and histologically 100 hearts of necropsy patients – 54 with a history of AF and 46 without AF. Premortem ECGs were evaluated. The patients with AF had significantly heavier heart, larger LA, more severely scarred myocardium of the LA and atrial septum, and more severe amyloidosis in both atria. Severity of amyloidosis was higher in LAs vs. right atria (RAs). Distribution of both fibrosis and amyloidosis was irregular. The most affected area was in the LA anterior wall. Patients with a history of AF and with most severe amyloidosis have more often abnormally long P waves. Finding of long P wave may contribute to diagnosis of a hitherto undisclosed atrial fibrillation.

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Abnormal P wave Morphology in the Electrocardiogram of a Hurdle Runner with a History of Presyncope - A Case Report

Medicina Sportiva ◽

10.2478/v10036-008-0009-7 ◽

2008 ◽

Vol 12 (2) ◽

pp. 46-48 ◽

Cited By ~ 2

Author(s):

Małgorzata Poręba ◽

Robert Skalik ◽

Rafał Poręba ◽

Paweł Gać ◽

Witold Pilecki ◽

...

Keyword(s):

Case Report ◽

P Wave ◽

Wave Morphology ◽

History Of

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

EP Europace ◽

10.1093/europace/euaa162.165 ◽

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.

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An Atrial Fibrillation with Normal Limits using Signal Processing- A Review

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.24.12133 ◽

2018 ◽

Vol 7 (2.24) ◽

pp. 453

Author(s):

S. Sathish ◽

K Mohanasundaram

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

P Wave ◽

Wave Duration ◽

Normal Limits ◽

Normal Sinus ◽

P Wave Duration ◽

Standard Lead ◽

Wave Morphology

Atrial fibrillation is an irregular heartbeat (arrhythmia) that can lead to the stroke, blood clots, heart failure and other heart related complications. This causes the symptoms like rapid and irregular heartbeat, fluttering, shortness of breath etc. In India for every around 4000 people eight of them are suffering from Atrial Fibrillation. P-wave Morphology. Abnormality of P-wave (Atrial ECG components) seen during sinus rhythm are associated with Atrial fibrillation. P-wave duration is the best predictor of preoperative atrial fibrillation. but the small amplitudes of atrial ECG and its gradual increase from isometric line create difficulties in defining the onset of P wave in the Standard Lead Limb system (SLL).Studies shows that prolonged P-wave have duration in patients (PAF) In this Study, a Modified Lead Limb (MLL) which solves the practical difficulties in analyzing the P-ta interval for both in healthy subjects and Atrial Fibrillation patients. P-Ta wave interval and P-wave duration can be estimated with following proposed steps which is applicable for both filtered and unfiltered atrial ECG components which follows as the clinical database trials. For the same the p-wave fibrillated signals that escalates the diagnosis follows by providing minimal energy to recurrent into a normal sinus rhythm.

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