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

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.  

scholarly journals Does Parkinson's Disease Increase the Risk of Atrial Fibrillation? Insights From Electrocardiogram and Risk Scores From a Case-Control Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana Alves ◽  
Ana Mafalda Abrantes ◽  
Gonçalo Portugal ◽  
M. Manuela Cruz ◽  
Sofia Reimão ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Case Control Study ◽  
Case Control ◽  
P Wave ◽  
Risk Scores ◽  
Wave Duration ◽  
P Wave Duration ◽  
Increased Risk ◽  
The Mean ◽  
Control Study

Background: Previous studies suggested that Parkinson's Disease (PD) patients could have an increased risk of atrial fibrillation. However, data supporting this association is not robust. We aimed to compare the potential risk of atrial fibrillation associated with PD in an age and gender matched case-control study, comparing the p-wave indexes from electrocardiograms and clinical risk scores among groups.Methods: A cross-sectional case-control study was performed. All subjects included in the analysis were clinically evaluated and subjected to a 12-lead electrocardiogram. Two blinded independent raters measured the p-wave duration. Subjects were classified as having normal P-wave duration (<120 ms), partial IAB (P-wave duration ≥ 120 ms, positive in inferior leads), and advanced IAB (p-wave duration ≥ 120 ms with biphasic morphology in inferior leads). Atrial fibrillation risk scores (CHARGE-AF, HATCH, and HAVOC) were calculated.Results: From 194 potential participants, three were excluded from the control group due to a previous diagnosis of atrial fibrillation. Comparing the PD patients (n = 97) with controls (n = 95), there were no statistically significant differences regarding the mean p-wave duration (121 ms vs. 122 ms, p = 0.64) and proportion of advanced interatrial block (OR = 1.4, 95%CI = 0.37–5.80, p = 0.58). All patients had a low or medium risk of developing atrial fibrillation based on the clinical scores. There were no differences between the PD patients and controls regarding the mean values of CHARGE-AF, HATCH, and HAVOC.Conclusions: Our results do not support the hypothesis that PD patients have an increased risk of atrial fibrillation based on the p-wave predictors and atrial fibrillation clinical scores.

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

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.

Multifocal atrial tachycardia

2018 ◽  
Author(s):  
Michael Jones ◽  
Norman Qureshi ◽  
Kim Rajappan
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Atrial Tachycardia ◽  
The Other ◽  
P Wave ◽  
Qrs Complex ◽  
P Waves ◽  
Ectopic Beats ◽  
Atrial Rhythm ◽  
Pathophysiologic Mechanisms

Multifocal atrial tachycardia (MAT) is an atrial arrhythmia arising in the left or right atrium, or both, with multiple different P wave morphologies (at least three), with an atrial rate usually faster than 100 min−1. The atrial rhythm may be irregular; however, the defining difference between MAT and atrial fibrillation is the presence of a P wave prior to each QRS complex in MAT (but the absence of P waves in atrial fibrillation). MAT may be compared to sinus rhythm with very frequent polymorphic atrial ectopic beats, and in fact similar pathophysiologic mechanisms underlie both conditions; thus, differentiating one from the other may be difficult—the principle difference is the lack of a single dominant sinus pacemaker in MAT.

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

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

P322Understanding the multiple P-wave morphologies in paroxysmal atrial fibrillation, during sinus rhythm, using computer simulation

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Filos ◽  
D Tachmatzidis ◽  
C Bakogiannis ◽  
D Mouselimis ◽  
A Tsarouchas ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Electrical Activity ◽  
Pulmonary Veins ◽  
P Wave ◽  
Electrical Remodeling ◽  
Atrial Remodeling ◽  
P Waves ◽  
Sa Node ◽  
Atrial Activation

Abstract Background Atrial Fibrillation (AF) is the most common atrial arrhythmia. The initiation and perpetuation of AF are related to atrial remodeling affecting the electrical and structural atrial characteristics. The beat-to-beat analysis of the P-wave morphology (PWM), during sinus rhythm (SR), revealed the existence of a secondary PWM, while the proportion of the P-waves which follow the secondary morphology is higher in patients with a history of paroxysmal AF (pAF). This observation has led to the hypothesis that the multiple PWM may be the result of a transient shift in the stimulus origin, possibly within the broader anatomical region of the sinoatrial (SA) node, and it is the atrial electrical remodeling that contributes to more frequent P-waves following a secondary morphology in patients with pAF. Purpose To better understand the pathophysiology of AF there is a need to link different levels of analysis, in order to interpret macroscopic observations, through a surface electrocardiogram, with changes occurring at cell and tissue level. Towards this direction, computational modeling can be used as it is a non-invasive and reproducible method of analyzing the electrical activity of the heart. Methods The CRN atrial model was used, and a two-dimensional geometry of the atrial architecture was considered, including the major anatomical structures, like Crista Terminalis, Pectinate Muscles and Pulmonary Veins. Using existing knowledge, the CRN model was adapted to describe the ionic properties of the atrial structures as well as the electrical remodeling occurring under pAF conditions. Several scenarios were considered related to the extent of the electrical remodeled tissue and Heart Rate (HR) values. The stimulation protocol was designed as 5 stimuli originated at a specific point within the SA node area whereas the sixth stimulus originated either at the same location or 1 mm far from the previous one. The temporal variations of the atrial activation as a result of the transient shift of the sixth stimulus origin were computed. Results In electrically remodeled tissue, the displacement of the excitation site within the SA node resulted in a significant increase of the differences in atrial activation compared to healthy tissue, and the greater the spatial extent of the remodeling the greater the differences in the completion of the electrophysiological processes. In addition, increased HR or HR variability led to the increase of the differences especially when electrical remodeling coexists. Conclusions The observed differences in atrial substrate activation can explain the increased number of P-waves that match a secondary PWM in pAF patients during SR, while a future perspective is to use PWM as a marker to estimate the electrical remodeling extent in the atrial tissue. These results underline the need to link the macroscopic findings to the suspected microscopic electrical activity in order to better understand the pathophysiology of AF.

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

EP Europace ◽  
2005 ◽  
Vol 7 (s2) ◽  
pp. S39-S48 ◽  
Author(s):  
Jonas Carlson ◽  
Rasmus Havmöller ◽  
Alberto Herreros ◽  
Pyotr Platonov ◽  
Rolf Johansson ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
P Wave ◽  
Signal Averaging ◽  
Wave Shape ◽  
Signal Shape ◽  
Clinical Routine ◽  
P Waves ◽  
Wave Morphology ◽  
History Of ◽  
Morphology Parameters

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.

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.

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