| 1. |
- Carlson, Jonas, et al.
(författare)
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Can Orthogonal Lead Indicators of Propensity to Atrial Fibrillation be Accurately Assessed from the 12-Lead ECG?
- 2005
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Ingår i: Europace. - : Oxford University Press (OUP). - 1532-2092. ; 7:Suppl 2, s. 39-48
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Tidskriftsartikel (refereegranskat)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 madebetween 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 alsoused 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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| 2. |
- Havmöller, Rasmus, et al.
(författare)
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Age-related changes in P wave morphology in healthy subjects.
- 2007
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Ingår i: BMC Cardiovascular Disorders. - : Springer Science and Business Media LLC. - 1471-2261. ; 7:22
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Tidskriftsartikel (refereegranskat)abstract
- Background We have previously documented significant differences in orthogonal P wave morphology between patients with and without paroxysmal atrial fibrillation (PAF). However, there exists little data concerning normal P wave morphology. This study was aimed at exploring orthogonal P wave morphology and its variations in healthy subjects. Methods 120 healthy volunteers were included, evenly distributed in decades from 20–80 years of age; 60 men (age 50+/-17) and 60 women (50+/-16). Six-minute long 12-lead ECG registrations were acquired and transformed into orthogonal leads. Using a previously described P wave triggered P wave signal averaging method we were able to compare similarities and differences in P wave morphologies. Results Orthogonal P wave morphology in healthy individuals was predominately positive in Leads X and Y. In Lead Z, one third had negative morphology and two-thirds a biphasic one with a transition from negative to positive. The latter P wave morphology type was significantly more common after the age of 50 (P < 0.01). P wave duration (PWD) increased with age being slightly longer in subjects older than 50 (121+/-13 ms vs. 128+/-12 ms, P < 0.005). Minimal intraindividual variation of P wave morphology was observed. Conclusion Changes of signal averaged orthogonal P wave morphology (biphasic signal in Lead Z), earlier reported in PAF patients, are common in healthy subjects and appear predominantly after the age of 50. Subtle age-related prolongation of PWD is unlikely to be sufficient as a sole explanation of this finding that is thought to represent interatrial conduction disturbances. To serve as future reference, P wave morphology parameters of the healthy subjects are provided.
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| 3. |
- Herreros, Alberto, et al.
(författare)
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An Algorithm for Phase-Space Detection of the P Characteristic Points
- 2007
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Ingår i: PROC. annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2007). Lyon, France, Aug 23-26, 2007. - 1557-170X. ; 1, s. 2004-2007
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Konferensbidrag (refereegranskat)abstract
- A new algorithm, based on embedding phase space, to detect the P-wave characteristic points of an ECG signal is reported in this paper. The multi-lead ECG is transformed into points of an embedding phase space where similar ECG morphologies are converted into phase space points that are close using some distance measure. The algorithm is robust with respect to the type of selected characteristic points (onset, peak and end), morphology changes, baseline oscillations and high frequency noise. The performance of the algorithm has been succesfully validated using both simulated and real ECG signals.
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| 4. |
- Herreros, Alberto, et al.
(författare)
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Analysis of Changes in the Beat-to-Beat P-wave Morphology Using Clustering Techniques
- 2009
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Ingår i: Biomedical Signal Processing and Control. - : Elsevier BV. - 1746-8094. ; 4:4, s. 309-316
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Tidskriftsartikel (refereegranskat)abstract
- Several pathologies related to the atrial electrical activity can be detected in the electrocardiogram P-wave. A protocol for analyzing P-wave morphology changes has been developed in this article. By using this protocol a study on the beat-to-beat P-wave morphology changes of 89 ECG signals is performed. An algorithmbased on the embedding space techniques has been used to extract the P-wave information of the ECG. The P-waves obtained in several of these ECGs exhibit significant alternate morphology changes. The morphologies have been classified by using the K-means clustering algorithm. The mechanism behind the P-wave morphology change process and its possible pathophysiological importance remains to be clarified.
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| 5. |
- Herreros, Alberto, et al.
(författare)
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Analysis of Changes in the Beat-to-Beat P-Wave Morphology Using Clustering Techniques
- 2008
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Ingår i: Proceedings of the 17th World Congress Proceedings of the 17th World Congress of The International Federation of Automatic Control. - 1474-6670. - 9783902661005 ; 41:1, s. 5215-5220
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Konferensbidrag (refereegranskat)abstract
- Several pathologies related to the atrial electrical activity can be detected in the electrocardiogram P-wave. A study on the beat-to-beat P-wave morphology changes of 89 ECG signals is performed in this article. An algorithm based on the embedding space techniques has been used to extract the P-wave information of the ECG. The P-waves obtained in several of these ECGs exhibit intermittent morphology changes. The morphologies have been classified by using the K-means clustering algorithm. The mechanism behind different P-wave morphologies and its possible pathophysiological importance remains to be clarified.
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| 6. |
- Herreros, Alberto, et al.
(författare)
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Performance Improvement of a Phase Space Detection Algorithm for Electrocardiogram Wave Morphology Classification
- 2011
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Ingår i: Journal of Electrocardiology. - 1532-8430. ; 44:2, s. 31-31
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Tidskriftsartikel (refereegranskat)abstract
- An algorithm based on embedding phase space signal was developed by the authors in previous works. The algorithm detects the characteristic points of the waves of a multilead electroacardiogram (ECG). In the present work, the parameters of this algorithm are optimized to improve its performance. The algorithm uses 2 configurable parameters to obtain the phase spacethe dimension of the phase space and the delayand a threshold to select the points of the ECG. By a proper selection of these parameters, the algorithm obtains all the points in the ECG that are similar to a reference one that was selected by the analyst. Several strategies have been developed and incorporated in the phase space algorithm to obtain the optimal values of these parameters based on the sampling rate and the number of leads in the records. The professional only needs to mark the reference point and the associated wave to it, for example, the start and end of a P wave and its peak. The algorithm obtains every P wave of the ECG record and a classification of their morphology using clustering techniques. Moreover, a simple graphical interface has been developed to ease its use. The algorithm was applied to detect the start, peak, and end of the P waves of a collection of ECG records of 6 minutes. Using this information, the algorithm extracts and classifies the P waves by applying clustering techniques to study their variability. The algorithm can also be used online to detect and classify different types of morphologies in any ECG wave. A future use of this algorithm will be the detection of several extracardiac pathologies in the ECG Holter, for example, sleep apnea.
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