Figure 1 - uploaded by José J Rieta
Content may be subject to copyright.
Examples displaying P-waves with normal (solid line) and abnormal (dotted line) morphologies, but with the same duration (i.e. 96 ms for P-waves in the right panel and 108 ms for P-waves in the left panel). As can be appreciated in both cases, the arc length is notably different for normal and abnormal P-waves.
Source publication
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 informat...
Contexts in source publication
Context 1
... that this parameter measures the rectified P-wave length, it can be useful to discern between normal and abnormal P-waves with similar duration, such as figure 1 shows for two typical examples. Indeed, as can be appreciated in both cases, normal and abnormal P waves present the same duration, but the arc length is notably higher for the wave with abnormal morphology. ...
Context 2
... time in advance with which a PAF episode onset can be predicted is today a relevant clinical challenge. This information could be useful in the development of tailored preventive treatments to avert the loss of normal sinus rhythm (Ishida et al 2010). To the best of our Table 2. Mean and standard deviation of the slope α computed from the analyzed parameters for the three considered ECG segment groups. ...
Context 3
... P-wave duration variability over time provided the highest statistical differences among groups of ECG segments as well as the best classification results. Discriminant abilities slightly higher than 90% and 80% between ECG segments from healthy subjects and PAF patients and between ECG segments far from PAF and close to PAF were achieved, respectively (Martínez et al 2012). Hence, the present work is the first study analyzing the P-wave morphological variability over time. ...
Context 4
... has been shown that the rectified P-wave length variability over time provided the highest statistical differences between groups of ECG segments and the best classification results from all the analyzed P-wave morphological features. Although the P al global accuracy for the three considered ECG segment groups was similar to those reported by the P-wave duration variability, that is around 80% (Martínez et al 2012), the arc length provided a considerably higher ability in discerning between ECG segments from healthy subjects and PAF patients (94.48%) and between ECG segments far from PAF and close to PAF onset (86.96%). Moreover, a notably wider database and a more robust statistical analysis has been carried out in the present study than in the previous aforesaid work, in which P-wave duration variability was analyzed (Martínez et al 2012). ...
Context 5
... that delays in atrial conduction velocity can provoke a similar increase both in the P-wave duration and its rectified length. However, as shown in figure 1, the arc length has the ability to discern between waves of similar duration remarkably different in morphology. Thus, high values of the rectified P-wave length can be due to the presence of bumps on its waveform, such as indicated in the examples of figure 1. ...
Context 6
... as shown in figure 1, the arc length has the ability to discern between waves of similar duration remarkably different in morphology. Thus, high values of the rectified P-wave length can be due to the presence of bumps on its waveform, such as indicated in the examples of figure 1. Overall, it can be considered that the P-wave arc length could provide additional morphological information to the one revealed by the P-wave duration. ...
Similar publications
Atrial fibrillation (AF) detection is crucial for stroke prevention. We investigated the potential of quantitative analyses of photoplethysmogram (PPG) waveforms to identify AF. Continuous electrocardiogram (EKG) and fingertip PPG were recorded simultaneously in acute stroke patients (n = 666) admitted to an intensive care unit. Each EKG was visual...
Purpose:
Comparisons of rhythm and rate control strategies for stroke prevention in patients with atrial fibrillation (AF) are still inconclusive. We compared differences in clinical outcomes between the rhythm and rate control strategies.
Materials and methods:
The COmparison study of Drugs for symptom control and complication prEvention of Atr...
A 69-year-old woman with palpitations was referred to our hospital for a second session of atrial fibrillation (AF) catheter ablation. She had a history of AF ablation including pulmonary vein (PV) isolation and persistent left superior vena cava (PLSVC) isolation. Electrophysiologic studies showed the veno-atrial connections that had recovered. Af...
Citations
... Recent studies have found that there exists an increment of the variability of P-wave morphology in subjects who are more prone to develop AF [47,48], as well as controls have less variability over time due to less amount of fragmentation and variability with respect to AF patients [49]. In our results, when assessing extracted features individually, cross correlation index of P-waves has shown to be significantly higher after catheter ablation for the 80% of the patients enrolled in the study. ...
The effects of pulmonary vein isolation in the surface electrocardiogram of patients with paroxysmal atrial fibrillation are analyzed in this paper using non-invasive markers for early detection of the arrhythmia recurrences.
Several features have been extracted on P-waves of V1 lead for paroxysmal atrial fibrillation patients who underwent catheter ablation of pulmonary veins for restoring sinus rhythm permanently. Surface ECG was simultaneously recorded along with intracardiac recordings starting from the beginning of the intervention until half an hour after the catheter ablation successfully ended. Significant difference between the means before and after catheter ablation have been observed for the cross-correlation index, kurtosis and amplitude dispersion of P-waves.
A logistic regression has been applied to all the descriptors and pointed to the amplitude dispersion index, as well as the minimum gradient joint with kurtosis of P-waves prior to catheter ablation as good predictors of recurrence of atrial fibrillation (78% accuracy). It is important to note how using a few descriptors good classification results are achieved. This study opens a door to early detection of atrial fibrillation recurrences using markers obtained by non-invasive methods.
... As regards the AF patients, the analysis of P-wave during SR is of clinical value, since P-wave morphology is affected by the site of origin of the SR beat and the atrial conduction routes (Platonov, 2012). The analysis of P-waves can reveal information related to the prediction of AF initiation (Martínez et al., 2014a) or the success of the pulmonary vein isolation (Huo et al., 2015). Also, the analysis of the P-wave morphology variability, on a beat-to-beat basis, also revealed differences between healthy subjects and AF patients (Censi et al., 2016;Filos et al., 2017). ...
The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.
https://www.frontiersin.org/article/10.3389/fphys.2019.00742
Frontiers in Physiology
... Conventionally, the R-R interval is used to data extraction from the ECG signal in order to diagnose different types of arrhythmia [3][4][5][6][7][8]. However, the analysis of the R-R intervals is not able to measure changes on other ECG waves, such as the distortions on P wave for atrial fibrillation (AF) [9][10][11][12]. Thereby, some studies segment the ECG signal [13][14][15][16][17]. Nevertheless, the diagnosis is still not ideal. ...
Background
Several authors use the R-R interval, which is the temporal difference between the largest waves (R waves) of the electrocardiogram (ECG), to propose a support system for the diagnosis of arrhythmias. However, R-R interval analysis does not measure ECG waveform deformations such as P wave deformations for atrial fibrillation.
Objective
In this study, we propose an arbitrary analysis the any segment of the heartbeat. This analysis is a generalization of a previous work that measures the wave deformations of the ECG signal.
Methods
We proposed to investigate the voltage (mV) variation occurring at each heartbeat interval using statistical moments. Unlike the R-R interval in which each heartbeat is associated with a single real number, the proposed method associates each heartbeat to a set of points, that is, a vector. The heartbeats were obtained in the following databases: MIT-BIH Normal Sinus Rhythm, MIT-BIH Atrial Fibrillation (AF), and MIT-BIH Arrhythmia; and the classifiers used to evaluate the proposed method were linear discriminant analysis, k-nearest neighbors, and support vector machine. The experiments were conducted using 80% of the patients for training (16 healthy patients, 41 patients with arrhythmia, and 20 patients with AF) and 20% of the patients for testing (2 healthy patients, 6 patients with arrhythmia, and 3 patients with AF).
Results
The proposed method proved to be efficient in solving global (accuracy is up to 99.78% in the arrhythmia classification) and local (accuracy of 100% in the AF classification) heartbeat problems.
Conclusion
The results obtained by the proposed method can be used to support decision-making in clinical practices.
... Future improvements of the TWA Simulator will include: multi-lead ECG traces being TWA a leaddependent phenomenon [16] and additional options for changing fundamental TWA frequency (for example every other three or more beats). Eventually, analogues simulators can be developed for alternans of the other ECG waves, such as QRS complex [17], P wave [18] and U wave [19]. For research purposes, TWA Simulator can be obtained for free by contacting the research manager (Laura Burattini, l.burattini@univpm.it). ...
T-wave alternans (TWA) is an every-other-beat fluctuation of the T-wave amplitude, often at microvolt (invisible) levels. It is recognized as an important risk index of severe ventricular arrhythmias, leading sometimes to sudden cardiac arrest. Many algorithms for automatic detection and quantification of TWA have been proposed; when applied to the same electrocardiogram (ECG), they may provide a different TWA quantification, making interpretation of differences difficult. Aim of this work is to propose TWA Simulator as a useful tool to validate and compare TWA identification methods. TWA Simulator is a user-friendly MATLAB graphical user interface (GUI) able to generate, model, visualize and store simulated ECG (SECG) affected by TWA of known morphology and amplitude. SECG is constructed by a N-fold repetition of a template, constituted by a real and clean ECG beat. Both number of beats and RR inter-beat variability can be set by the user. Both direct and inverted TWA can be simulated. Direct TWA is simulated by adding a waveform (among four possibility) to every other T wave; inverted TWA is simulated by changing T-wave polarity in every-other SECG beat. Availability of TWA Simulator would allow efficient validation and comparison of automatic TWA identification methods by helping interpretation of results.
... Future improvements of the TWA Simulator will include: multi-lead ECG traces being TWA a leaddependent phenomenon [16] and additional options for changing fundamental TWA frequency (for example every other three or more beats). Eventually, analogues simulators can be developed for alternans of the other ECG waves, such as QRS complex [17], P wave [18] and U wave [19]. For research purposes, TWA Simulator can be obtained for free by contacting the research manager (Laura Burattini, l.burattini@univpm.it). ...
T-wave alternans (TWA) is an every-other-beat fluctuation of the T-wave amplitude, often at microvolt (invisible) levels. It is recognized as an important risk index of severe ventricular arrhythmias, leading sometimes to sudden cardiac arrest. Many algorithms for automatic detection and quantification of TWA have been proposed; when applied to the same electrocardiogram (ECG), they may provide a different TWA quantification, making interpretation of differences difficult. Aim of this work is to propose TWA Simulator as a useful tool to validate and compare TWA identification methods. TWA Simulator is a user-friendly MATLAB graphical user interface (GUI) able to generate, model, visualize and store simulated ECG (SECG) affected by TWA of known morphology and amplitude. SECG is constructed by a N-fold repetition of a template, constituted by a real and clean ECG beat. Both number of beats and RR inter-beat variability can be set by the user. Both direct and inverted TWA can be simulated. Direct TWA is simulated by adding a waveform (among four possibility) to every other T wave; inverted TWA is simulated by changing T-wave polarity in every-other SECG beat. Availability of TWA Simulator would allow efficient validation and comparison of automatic TWA identification methods by helping interpretation of results.
... The problem of detecting and predicting PAF events from ECG recordings received attention from researchers from different fields [5]. Many approaches have been proposed to tackle such problems, including the use of entropy descriptors [6], [7], P-wave characterization [8]- [10], the number of premature atrial complexes from R-R intervals [11] (i.e., the interval between R peaks, shown in Fig. 2), heart variability rate [12], the average number of f-waves in a TQ interval [13], and hybrid complex networks / time series analysis techniques [14], [15]. ...
... The problem of detecting and predicting PAF events from ECG recordings received attention from researchers from different fields [5]. Many approaches have been proposed to tackle such problems, including the use of entropy descriptors [6], [7], P-wave characterization [8]- [10], the number of premature atrial complexes from R-R intervals [11] (i.e., the interval between R peaks, shown in Fig. 2), heart variability rate [12], the average number of f-waves in a TQ interval [13], and hybrid complex networks / time series analysis techniques [14], [15]. ...
We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to be design a suitable procedure for detecting and, more importantly, predicting PAF episodes. We propose a method for predicting PAF events whose first step consists of a feature extraction procedure that represents each ECG as a multi-variate time series. Successively, we design a classification framework based on kernel similarities for multi-variate time series, capable of handling missing data. We consider different approaches to perform classification in the original space of the multi-variate time series and in an embedding space, defined by the kernel similarity measure. Our classification results show state-of-the-art performance in terms of accuracy. Furthermore, we demonstrate the ability to predict, with high accuracy, the PAF onset up to 15 minutes in advance.
... This parameter discerns between the presence or absence of SCR components, and it has been used previously in the morphological characterization of Gaussian responses [32]. Moreover, some parameters related to the SCR amplitude are also used to assess the activation of the sympathetic nervous system. ...
This article introduces a new and unobtrusive wearable monitoring device based on electrodermal activity (EDA) to be used in health-related computing systems. This paper introduces the description of the wearable device capable of acquiring the EDA of a subject in order to detect his/her calm/distress condition from the acquired physiological signals. The lightweight wearable device is placed in the wrist of the subject to allow continuous physiological measurements. With the aim of validating the correct operation of the wearable EDA device, pictures from the International Affective Picture System are used in a control experiment involving fifty participants. The collected signals are processed, features are extracted and a statistical analysis is performed on the calm/distress condition classification. The results show that the wearable device solely based on EDA signal processing reports around 89% accuracy when distinguishing calm condition from distress condition.
... The idea behind beat-to-beat (b2b) analysis is that mild to moderate remodeling may not constantly change the P-wave morphology. Increased b2b variability of time domain and other morphological parameters, such as P-wave velocity, arc and area, were associated with the approximation of PAF episodes [25,26] , suggesting a distributed conduction in the atrial substrate. Other studies combine the b2b analysis of the P-wave with correlation techniques in order to explore the P-wave morphology. ...
Background and objectives:
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects.
Methods:
Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3–5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset.
Results:
In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction.
Conclusion:
The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria.
For more, see here: https://authors.elsevier.com/c/1VfZ6cV4Kti7~
... Increased P wave duration and P wave dispersion reflect prolongation of intra-atrial and inter-atrial conduction time and the inhomogeneous propagation of sinus impulses [7], which are electro-physiologic characteristics in patients with AF during non-AF period. Martinez et al. [8] found that P wave fragmentation and P wave area presented a higher variability 2 h preceding the onset of paroxysmal atrial fibrillation. Asgari et al. [9] achieved a sensitivity of 97.0% and a specificity of 97.1% by using stationary wavelet transform of ECG waveform and support vector machine for the MIT-BIH atrial fibrillation database. ...
... (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length, because the accuracy is equal to the specificity according to formulas (8) and (10) when TP = 0 and FN = 0. Besides, both PPV and sensitivity are not accessible for the MIT-BIH normal sinus rhythm database. ...
Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20 ms fixed resolution in y-axes and 15-60 s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen's Kappa coefficients were 0.87, 0.91 and 0.64 at 50 s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60 s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40 s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.
