P-Wave Morphology Assessment by a Gaussian Functions-Based Model in Atrial Fibrillation Patients

Post-Ischemic Stroke Cardiovascular Risk Prevention and Management

Cardiac death is the second most common cause of death among patients with acute ischemic stroke (IS), following neurological death resulting directly from acute IS. Risk prediction models and screening tools including electrocardiograms can assess the risk of adverse cardiovascular events after IS. Prolonged heart rate monitoring and early anticoagulation therapy benefit patients with a higher risk of adverse events, especially stroke patients with atrial fibrillation. IS and cardiovascular diseases have similar risk factors which, if optimally managed, may reduce the incidence of recurrent stroke and other major cardiovascular adverse events. Comprehensive risk management emphasizes a healthy lifestyle and medication therapy, especially lipid-lowering, glucose-lowering, and blood pressure-lowering drugs. Although antiplatelet and anticoagulation therapy are preferred to prevent cardiovascular events after IS, a balance between preventing recurrent stroke and secondary bleeding should be maintained. Optimization of early rehabilitation care comprises continuous care across environments thus improving the prognosis of stroke survivors.

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims

Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG).

Methods and results

Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%.

Conclusion

Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation

Background

Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model.

Results

This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911.

Conclusion

In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.

A Detector for Premature Atrial and Ventricular Complexes

The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers.

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

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

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.

Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation

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.

Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes

OBJECTIVE

A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed.

SIGNIFICANCE

The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking.

APPROACH

The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise.

MAIN RESULTS

Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties.

Beat-to-beat variability of P-wave in patients suffering from atrial fibrillation

The aim of this paper was to analyze the P-wave variability over time in patients suffering from Atrial Fibrillation (AF). Behind some time-domain and morphological features of the P-wave template, two novel indexes of P-wave variability have been estimated: one based on the cross-correlation coefficients among the P-waves (Correlation Index, CI), and one associated to variation of P-waves amplitude (Amplitude Index, AI). These indexes were estimated in two experimental models: patients suffering from persistent AF respect to control subjects and patients developing post-operative AF (POAF) after coronary artery bypass grafting respect to patients without POAF. The control group resulted to be characterized by shorter P-wave duration and by a less amount of fragmentation and variability, respect to AF patients (with a sensitivity and specificity of 98.4% and 95 % respectively). Also P-wave features resulted to be different for patients with POAF respect to patients without. In conclusion the quantification of the P-wave variability over time can add information in the understanding of the association between the anatomical atrial substrate and atrial arrhythmias.

Decomposition of fractionated local electrograms using an analytic signal model based on sigmoid functions

Microstructural heterogeneities in cardiac tissue, such as embedded connective tissue secondary to fibrosis, may lead to complex patterns of electrical activation that are reflected in the fractionation of extracellularly recorded electrograms. The decomposition of such electrograms into non-fractionated components is expected to provide additional information to allow a more precise classification of the microstructural properties adjacent to a given recording site. For the sake of this, an analytic signal model is introduced in this study that is capable of reliably identifying extracellular waveforms associated with sites of initiating, free-running, and terminating or colliding activation wavefronts. Using this signal model as a template, a procedure is developed for the automatic decomposition of complex fractionated electrograms into non-fractionated components. The decomposition method has been validated using electrograms obtained from one- and two-dimensional computer simulations in which all relevant intracellular and extracellular quantities are accessible at a very high spatiotemporal resolution and can be manipulated in a controlled manner. Fractionated electrograms were generated in these models by incorporating microstructural obstacles that mimicked inlays of connective tissue. Using this signal model, fractionated electrograms emerging from microstructural heterogeneities in the submillimeter range with latencies between components down to 0.6 ms can be decomposed.

Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation

Few data are available on the assessment of P-wave beat-to-beat morphology variability and its ability to identify patients prone to paroxysmal atrial fibrillation (AF) occurrence. Aim of this study was to determine whether electrocardiographic (ECG) parameters resulting from the beat-to-beat analysis of P wave in ECG recorded during sinus rhythm could be indicators of paroxysmal AF susceptibility. ECGs of 76 consecutive patients including 36 patients with history of AF and no overt structural cardiac abnormalities and a control group of 40 healthy patients without history of AF were analyzed. After preprocessing, features based on P waves and RR intervals were extracted from lead II of a 5-minute ECG recorded during sinus rhythm. The discriminative power of the extracted features was assessed. Among extracted features, the most discriminative ones to identify patients with paroxysmal episodes of AF were the mean P-wave duration and the SD of beat-to-beat Euclidean distance between P waves (an indicator of beat-to-beat P-wave morphologic variability). Patients with history of AF presented a significantly longer P-wave duration (125 ± 18 vs 110 ± 8 ms, p <0.001) and higher variability of P-wave morphology over time (beat-to-beat Euclidean distance: 0.11 ± 0.07 vs 0.076 ± 0.02, p <0.01) compared to patients without history of AF. Combination of P-wave duration and standard deviation of beat-to-beat Euclidean distance led to an accuracy of 88% in the discrimination between the 2 groups of patients. In conclusion, combination of P-wave duration and beat-to-beat Euclidean distance between P waves efficiently discriminates patients with history of AF and no overt structural cardiac abnormalities from healthy age-matched subjects, and it might be used as an effective tool to identify patients prone to paroxysmal AF occurrence.

P-wave Variability and Atrial Fibrillation

The analysis of P-wave template has been widely used to extract indices of Atrial Fibrillation (AF) risk stratification. The aim of this paper was to assess the potential of the analysis of the P-wave variability over time in patients suffering from atrial fibrillation. P-wave features extracted from P-wave template together with novel indices of P-wave variability have been estimated in a population of patients suffering from persistent AF and compared to those extracted from control subjects. We quantify the P-wave variability over time using three algorithms and we extracted three novel indices: one based on the cross-correlation coefficients among the P-waves (Cross-Correlation Index, CCI), one associated to variation in amplitude of the P-waves (Amplitude Dispersion Index, ADI), one sensible to the phase shift among P-waves (Warping Index, WI). The control group resulted to be characterized by shorter P-wave duration and by a less amount of fragmentation and variability, respect to AF patients. The parameter CCI shows the highest sensitivity (97.3%) and a good specificity (95%).

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.

Impact of hybrid procedure on P wave duration for atrial fibrillation ablation

AIM

Hybrid procedure (HP) involves epicardial isolation of pulmonary vein and posterior wall of left atrium, and endocardial checking of lesions and touchups (if needed). We aimed at observing the effect of hybrid procedure on P wave duration (PWD), calculated automatically from surface ECG leads at start and end of HP, and also for relationship to atrial fibrillation (AF) recurrence at 9 months.

METHODS

Forty-one patients (32 male; mean age, 58.4 ± 9.5 years) underwent HP, as first ever ablation. A new automated method was used for P wave segmentation and PWD estimation from recognizable P waves in ECG lead I or II before and after HP, based on fitting of each P wave by means of two Gaussian functions.

RESULTS

Overall, PWD was significantly decreased after procedure (104.4 ± 25.1 ms vs. 84.7 ± 23.8 ms, p = 0.0151), especially in persistent AF patients (122.4 ± 32.2 ms vs. 85.6 ± 24.5 ms, p = 0.02). PWD preprocedure was significantly higher in persistent than in paroxysmal patients (122.4 ± 32.2 ms vs. 92.5 ± 17.9 ms, p = 0.0383). PWD was significantly decreased after procedure in prior electrical cardioverted patients (106.7 ± 30.5 ms vs. 84.7 ± 23.1 ms, p = 0.0353). After 9-month follow-up of 40 patients, HP-induced PWD decrease was significant for the 12 persistent patients without recurrence (122.4.1 ± 35.3 ms vs. 85.6 ± 22.0 ms, p = 0.0210).

CONCLUSION

Preprocedure PWD was higher for persistent than paroxysmal patients. HP reduced PWD significantly. Nine-month follow-up suggests that HP is successful in restoring and maintaining sinus rhythm. To individualize AF therapy, AF type-based selection of patients may be possible before procedure. Automated analysis of PWD from surface ECG is possible.

The P Wave Time-Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation

BACKGROUND

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.

METHODS

The longest sinus rhythm interval from 24-hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1-hour periods. Every single P wave was automatically delineated and characterized by 16 time and frequency metrics, such as its duration, absolute energy in several frequency bands and high-to-low-frequency energy ratios. Finally, the P wave variability over each 1-hour period was estimated from the 16 features making use of a least-squares linear fitting. As a reference, the same parameters were also estimated from a set of 1-hour ECG segments randomly chosen from a control group of 53 healthy subjects age-, gender-, and heart rate-matched.

RESULTS

All the analyzed metrics provided an increasing P wave variability trend as the onset of PAF approximated, being P wave duration and P wave high-frequency energy the most significant individual metrics. The linear fitting slope α associated with P wave duration was (2.48 ± 1.98)×10(-2) for healthy subjects, (23.8 ± 14.1)×10(-2) for ECG segments far from PAF and for (81.8 ± 48.7)×10(-2) ECG segments close to PAF p = 6.96×10(-22) . Similarly, the P wave high-frequency energy linear fitting slope was (2.42 ± 4.97)×10(-9) , (54.2 ± 107.1)×10(-9) and (274.2 ± 566.1)×10(-9) , respectively (p = 2.85×10(-20) ). A univariate discriminant analysis provided that both P wave duration and P wave high-frequency energy could discern among the three ECG sets with diagnostic ability around 80%, which was improved up to 88% by combining these metrics in a multivariate discriminant analysis.

CONCLUSION

Alterations in atrial conduction can be successfully quantified several hours before the onset of PAF by estimating variability over time of several time and frequency P wave features.

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.

P-wave characteristics after electrical external cardioversion: predictive indexes of relapse

Atrial fibrillation (AF) is the most common arrhythmia in the western countries and accounts for hundred thousand strokes per year. Electrocardiographic characteristics of AF have been demonstrated to help identify patients at risk of developing AF. Prolonged and highly fragmented P-waves have been observed in patients prone to AF, and time-domain. Morphological characteristics of the P-wave from surface ECG recordings turned out to significantly distinguish patients at risk of AF. The aim of this study is to evaluate the morphological and time-domain characteristics of the P-wave in patients with AF relapse after cardioversion, respect to patients without. 14 patients who underwent successful electrical cardioversion for persistent AF were enrolled. Five minute ECG recordings were performed for each subject, immediately post-successful cardioversion. ECG signals were acquired by using a 16-lead mapping system for high-resolution biopotential measurements (sample frequency 2 kHz, 31 nV resolution, 0-400 Hz bandwidth). From the 16 recordings, a standard 12-lead ECG was derived and analyzed in terms of signal-averaged P-wave. Time-domain and mor-phological characteristics were estimated from the averaged P-waves of each lead. Time-domain features were quantified as: maximum P-wave duration in any of the 12 leads (Pmax), minimum P-wave duration in any of the leads (Pmin), P-wave dispersion (Pdisp=Pmax-Pmin), and Pindex (standard devia-tion of P-wave duration in any of the 12 leads). Morphological characteristics were extracted from a Gaussian function-based model of the P-wave as: average model order (Nav), maximum number of zero-crossing (PCmax), and maximum and average number of maxima and minima (FCImax and FCIav) in any of the leads. The results obtained so far indicate that the morphological and time-domain characteristics distinguish between patients with AF relapse and patients without.

Beat to beat wavelet variability in atrial fibrillation

Atrial fibrillation (AF) is a complex phenomenon, related with a multitude of factors, including the electrical properties of the atrial substrate. The purpose of this work is to present a method that highlights electrocardiographic differences between normal subjects and patients with paroxysmal AF episodes (PAF), potentially related with substrate differences. Vectorcardiography recordings are considered and, for each lead (X-Y-Z), on a beat by beat basis, a steady window before QRS, corresponding to the atrial activity, is analysed via continuous wavelet transform. Wavelet-based parameters are calculated and compared between the normal and AF group, with the beat to beat variation of wavelet energy as the most important feature showing a significantly higher variability in the AF group.

Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram

Electrocardiogram (ECG) is characterized by a recurrent wave sequence of P, QRS and T-wave associated with each beat. The performance of the computer-aided ECG analysis systems depends heavily upon the accurate and reliable detection of these component waves. This paper presents an efficient method for the detection of P- and T-waves in 12-lead ECG using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander. SVM is used as a classifier for the detection of P- and T-waves. The algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate of 95.43% is achieved for P-wave detection and 96.89% for T-wave detection. The method successfully detects all kind of morphologies of P- and T-waves. The on-sets and off-sets of the detected P- and T-waves are found to be within the tolerance limits given in CSE library.

Time-domain and morphological analysis of the P wave. Part II: effects of atrial pacing on P-wave features

BACKGROUND

The aim of this study was to compare time-domain and morphological descriptors of paced and spontaneous P wave in patients prone to atrial fibrillation (AF).

METHODS

Nineteen patients (nine women, aged 72 +/- 10 years) affected by paroxysmal AF and implanted with dual-chamber pacemakers (PM) were studied. Two 5-minute recordings were performed during spontaneous and paced rhythm. Electrocardiogram (ECG) signals were acquired using a 32-lead mapping system. Patients were grouped into two classes: no previous AF and previous AF groups, according to the number of AF episodes in the 6 months before the analysis.

RESULTS AND CONCLUSION

During atrial pacing P wave appeared prolonged and morphologically more complex with respect to sinus rhythm. We also found that in patients at lower risk for AF, the atrial pacing changes the atrial activation to a greater extent than in patients at higher risk for AF. Finally, all time-domain and morphological descriptors of the P wave except one succeed in discriminating "no previous AF" and "previous AF" patients in spontaneous rhythm, while no significant differences have been observed during pacing for any parameters.