Arturo Martínez

Application of the phasor transform for automatic delineation of single-lead ECG fiducial points

This work introduces a new single-lead ECG delineator based on phasor transform. The method is characterized by its robustness, low computational cost and mathematical simplicity. It converts each instantaneous ECG sample into a phasor, and can precisely manage P and T waves, which are of notably lower amplitude than the QRS complex. The method has been validated making use of synthesized and real ECG sets, including the MIT-BIH arrhythmia, QT, European ST-T and TWA Challenge 2008 databases. Experiments with the synthesized recordings reported precise detection and delineation performances in a wide variety of ECGs, with signal-to-noise ratios of 10 dB and above. For real ECGs, the QRS detection was characterized by an average sensitivity of 99.81% and positive predictivity of 99.89%, for all the analyzed databases (more than one million beats). Regarding delineation, the maximum localization error between automatic and manual annotations was lower than 6 ms and its standard deviation was in agreement with the accepted tolerances for expert physicians in the onset and offset identification for QRS, P and T waves. Furthermore, after revising and reannotating some ECG recordings by expert cardiologists, the delineation error decreased notably, becoming lower than 3.5 ms, on average, and reducing by a half its standard deviation. This new proposed strategy outperforms the results provided by other well-known delineation algorithms and, moreover, presents a notably lower computational cost.

Study on the P-wave feature time course as early predictors of paroxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, increasing the risk of stroke and all-cause mortality. Its mechanisms are poorly understood, thus leading to different theories and controversial interpretation of its behavior. In this respect, it is unknown why AF is self-terminating in certain individuals, which is called paroxysmal AF (PAF), and not in others. Within the context of biomedical signal analysis, predicting the onset of PAF with a reasonable advance has been a clinical challenge in recent years. By predicting arrhythmia onset, the loss of normal sinus rhythm could be addressed by means of preventive treatments, thus minimizing risks for the patients and improving their quality of life. Traditionally, the study of PAF onset has been undertaken through a variety of features characterizing P-wave spatial diversity from the standard 12-lead electrocardiogram (ECG) or from signal-averaged ECGs. However, the variability of features from the P-wave time course before PAF onset has not been exploited yet. This work introduces a new alternative to assess time diversity of the P-wave features from single-lead ECG recordings. Furthermore, the method is able to assess the risk of arrhythmia 1 h before its onset, which is a relevant advance in order to provide clinically useful PAF risk predictors. Results were in agreement with the electrophysiological changes taking place in the atria. Hence, P-wave features presented an increasing variability as PAF onset approximates, thus suggesting intermittently disturbed conduction in the atrial tissue. In addition, high PAF risk prediction accuracy, greater than 90%, has been reached in the two considered scenarios, i.e. discrimination between healthy individuals and PAF patients and between patients far from PAF and close to PAF onset. Nonetheless, more long-term studies have to be analyzed and validated in future works.

Morphological variability of the P-wave for premature envision of paroxysmal atrial fibrillation events

The present work introduces the first study on the P-wave morphological variability two hours preceding the onset of paroxysmal atrial fibrillation (PAF). The development of non-invasive methods able to track P-wave alterations over time is a clinically relevant tool to anticipate as much as possible the envision of a new PAF episode. This information is essential for further improvement of preventive and patient-tailored treatment strategies, which could avert the loss of sinus rhythm. In this way, risks for the patients could be minimized and their quality of life improved. Recently, the P-wave morphological analysis is drawing increasing attention because differences in morphology can reflect different atrial activation patterns. Indeed, the P-wave morphology study has recently proved to be useful for determining the presence of an underlying pathophysiological condition in patients prone to atrial fibrillation. However, the P-wave morphology variability over time has not been studied yet. In this respect, the present work puts forward some parameters related to the P-wave shape and energy with the ability to quantify non-invasively the notable atrial conduction alterations preceding the onset of PAF. Results showed that P-wave fragmentation and area presented higher variability over time as the onset of PAF approximates. By properly combining these indices, an average global accuracy of 86.33% was achieved to discern between electrocardiogram segments from healthy subjects, far from a PAF episode and less than one hour close to a PAF episode. As a consequence, the P-wave morphology long-term analysis seems to be a useful tool for the non-invasive envision of PAF onset with a reasonable anticipation. Nonetheless, further research is required to corroborate this finding and to validate the capability of the proposed P-wave metrics in the earlier prediction of PAF onset.

Short-time regularity assessment of fibrillatory waves from the surface ECG in atrial fibrillation

This paper proposes the first non-invasive method for direct and short-time regularity quantification of atrial fibrillatory (f) waves from the surface ECG in atrial fibrillation (AF). Regularity is estimated by computing individual morphological variations among f waves, which are delineated and extracted from the atrial activity (AA) signal, making use of an adaptive signed correlation index. The algorithm was tested on real AF surface recordings in order to discriminate atrial signals with different organization degrees, providing a notably higher global accuracy (90.3%) than the two non-invasive AF organization estimates defined to date: the dominant atrial frequency (70.5%) and sample entropy (76.1%). Furthermore, due to its ability to assess AA regularity wave to wave, the proposed method is also able to pursue AF organization time course more precisely than the aforementioned indices. As a consequence, this work opens a new perspective in the non-invasive analysis of AF, such as the individualized study of each f wave, that could improve the understanding of AF mechanisms and become useful for its clinical treatment.

Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation.

The analysis of P-wave variability from the electrocardiogram (ECG) has been suggested as an early predictor of the onset of paroxysmal atrial fibrillation (PAF). Hence, a preventive treatment could be used to avoid the loss of normal sinus rhythm, thus minimising health risks and improving the patients quality of life. In these previous studies the variability of different temporal and morphological P-wave features has been only analysed in a linear fashion. However, the electrophysiological alteration occurring in the atria before the onset of PAF has to be considered as an inherently complex, chaotic and non-stationary process. This work analyses the presence of non-linear dynamics in the P-wave progression before the onset of PAF through the application of the central tendency measure (CTM), which is a non-linear metric summarising the degree of variability in a time series. Two hour-length ECG intervals just before the arrhythmia onset belonging to 46 different PAF patients were analysed. In agreement with the invasively observed inhomogeneous atrial conduction preceding the onset of PAF, CTM for all the considered P-wave features showed higher variability when the arrhythmia was closer to its onset. A diagnostic accuracy around 80% to discern between ECG segments far from PAF and close to PAF was obtained with the CTM of the metrics considered. This result was similar to previous P-wave variability methods based on linear approaches. However, the combination of linear and non-linear methods with a decision tree improved considerably their discriminant ability up to 90%, thus suggesting that both dynamics could coexist at the same time in the fragmented depolarisation of the atria preceding the arrhythmia.

Detection and removal of ventricular ectopic beats in atrial fibrillation recordings via principal component analysis

Ectopic beats are early heart beats with remarkable large amplitude that provoke serious disturbances in the analysis of electrocardiograms (ECG). These beats are very common in atrial fibrillation (AF) and are the source of important residua when the QRST is intended to be removed. Given that QRST cancellation is a binding step in the appropriate analysis of atrial activity (AA) in AF, a method for ventricular ectopic beats cancellation is proposed as a previous step to the application of any QRST removal technique. First, the method discriminates between normal and ectopic beats with an accuracy higher than 99% through QRS morphological characterization. Next, the most similar ectopic beats to the one under cancellation are clustered and serve to get their eigenvector matrix by principal component analysis. Finally, the highest variance eigenvector is used as cancellation template. The reduction ectopic rate (RER) has been defined to evaluate the methods performance by using templates generated with 5, 10, 20, 40 or 80 ectopics. Optimal results were reached with the 5 most similar complexes, yielding a RER higher than 5.5. In addition, a decreasing RER trend was noticed as the number of considered ectopics for cancellation increased. As conclusion, given that ectopics presented a remarkable variability in their morphology, the proposed cancellation approach is a robust ectopic remover and can notably facilitate the later application of any QRST cancellation technique to extract the AA in the best conditions.

Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation

This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave. In order to validate the algorithm, electrocardiogram segments 2 h preceding the onset of PAF episodes from 46 different patients were assessed. According to the expected intermittently disturbed atrial conduction before the onset of PAF, all the analyzed Gaussian metrics showed an increasing variability trend as the PAF onset approximated. Moreover, the Gaussian P-wave width reported a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF, and patients less than 1 h close to a PAF episode. This discriminant power was similar to those provided by the most classical time-domain approach, i.e., the P-wave duration. However, this newly proposed parameter presents the advantage of being less sensitive to a precise delineation of the P-wave boundaries. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 86.69%. In conclusion, morphological P-wave characterization provides additional information to the metrics based on P-wave timing.

Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation

A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients.

The P wave time-frequency variability reflects atrial conduction defects before paroxysmal atrial fibrillation

The study of atrial conduction defects associated with the onset of paroxysmal atrial fibrillation (PAF) can be addressed by analyzing the P wave from the surface electrocardiogram (ECG). Traditionally, signal‐averaged ECGs have been mostly used for this purpose. However, this alternative hinders the possibility to quantify every single P wave, its variability over time, as well as to obtain complimentary and evolving information about the arrhythmia. This work analyzes the time progression of several time and frequency P wave features as potential indicators of atrial conduction variability several hours preceding the onset of PAF.

A new method for automatic delineation of ECG fiducial points based on the Phasor Transform

The present work introduces a new ECG delineator, based on the Phasor Transform, which is characterized by its robustness, low computational cost and mathematical simplicity. The method converts each instantaneous ECG sample into a phasor, thus being able to deal very precisely with P and T waves, which are of notably lower amplitude than the QRS complex. Initially, the method relies on the detection of R peaks and, next, onset and offset of the QRS complex are identified. Finally, taking the QRS as a reference, P and T waves are detected and delineated. The new delineator has been validated with the QT database, providing average values of sensitivity higher than 98.60% for the detection of all the significant ECG waves and fiducial points. Additionally, the average maximum time delineation error was lower than 6 ms and its standard deviation was in agreement with the accepted tolerances for expert physicians in the onset and offset identification for QRS, P and T waves. As a consequence, this new algorithm is able to achieve similar performance to the one provided by other well known delineation algorithms, but with notably lower computational cost.