Juan Pablo Martínez

A wavelet-based ECG delineator: evaluation on standard databases

In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

Microvolt T-wave alternans physiological basis, methods of measurement, and clinical utility--consensus guideline by International Society for Holter and Noninvasive Electrocardiology.

This consensus guideline was prepared on behalf of the International Society for Holter and Noninvasive Electrocardiology and is cosponsored by the Japanese Circulation Society, the Computers in Cardiology Working Group on e-Cardiology of the European Society of Cardiology, and the European Cardiac Arrhythmia Society. It discusses the electrocardiographic phenomenon of T-wave alternans (TWA) (i.e., a beat-to-beat alternation in the morphology and amplitude of the ST-segment or T-wave). This statement focuses on its physiological basis and measurement technologies and its clinical utility in stratifying risk for life-threatening ventricular arrhythmias. Signal processing techniques including the frequency-domain Spectral Method and the time-domain Modified Moving Average method have demonstrated the utility of TWA in arrhythmia risk stratification in prospective studies in >12,000 patients. The majority of exercise-based studies using both methods have reported high relative risks for cardiovascular mortality and for sudden cardiac death in patients with preserved as well as depressed left ventricular ejection fraction. Studies with ambulatory electrocardiogram-based TWA analysis with Modified Moving Average method have yielded significant predictive capacity. However, negative studies with the Spectral Method have also appeared, including 2 interventional studies in patients with implantable defibrillators. Meta-analyses have been performed to gain insights into this issue. Frontiers of TWA research include use in arrhythmia risk stratification of individuals with preserved ejection fraction, improvements in predictivity with quantitative analysis, and utility in guiding medical as well as device-based therapy. Overall, although TWA appears to be a useful marker of risk for arrhythmic and cardiovascular death, there is as yet no definitive evidence that it can guide therapy.

Methodological principles of T wave alternans analysis: a unified framework

Visible T wave alternans (TWA) in the electrocardiogram (ECG) had been regarded as an infrequent phenomenon during the first 80 years of electrocardiography. Nevertheless, computerized analysis changed this perception. In the last two decades, a variety of techniques for automatic TWA analysis have been proposed. These techniques have allowed researchers to detect nonvisible TWA in a wide variety of clinical and experimental conditions. Such studies have recently shown that TWA is related to cardiac instability and increased arrhythmogenicity. Comparison of TWA analysis methods is a difficult task due to the diversity of approaches. In this paper, we propose a unified framework which holds the existing methods. In the light of this framework, the methodological principles of the published TWA analysis schemes are compared and discussed. This framework may have an important role to develop new approaches to this problem.

Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria

In this paper, we studied and validated a simple heart beat classifier based on ECG feature models selected with the focus on an improved generalization capability. We considered features from the RR series, as well as features computed from the ECG samples and different scales of the wavelet transform, at both avail able leads. The classification performance and generalization were studied using publicly available databases: the MIT-BIH Arrhythmia, the MIT-BIH Supraventricular Arrhythmia, and the St. Pe tersburg Institute of Cardiological Technics (INCART) databases. The Association for the Advancement of Medical Instrumentation recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found comprehends eight features, was trained in a partition of the MIT-BIH Arrhythmia, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93%; for normal beats, sensitivity (S) 95%, positive predictive value (P+) 98%; for supraventricular beats, S 11%, P+ 39%; and for ventricular beats S 81%, P+ 87%. In order to test the generalization capability, performance was also evaluated in the INCART, with results comparable to those obtained in the test set. This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability.

Characterization of repolarization alternans during ischemia: time-course and spatial analysis

T-wave alternans (TWA) has been linked to increased vulnerability to ventricular fibrillation in different settings including myocardial ischemia. In this study, we propose a methodology for the characterization of TWA induced by transient, regional ischemia. We studied the prevalence, magnitude and spatio-temporal relationship between TWA and ischemia in 95 patients undergoing percutaneous transluminal coronary angioplasty (PTCA). Two electrocardiogram records of each patient, a control recording before PTCA and the PTCA record, were analyzed using a robust, recently proposed method for TWA analysis. The detected episodes were characterized in terms of their time-course, lead distribution and alternans waveform. One third of the patients (33.7%) showed TWA episodes during PTCA. The highest prevalence (51.7%) and amplitude were found in patients with left anterior descendent artery occlusion. The onset of TWA was detected after the first 1-2 min of occlusion, suggesting that some level of ischemia must be attained before TWA arises, while disappearance of TWA following reperfusion was much more rapid. The TWA lead distributions and waveforms showed distinct distributions according to the occluded artery reflecting the regional nature of the TWA phenomenon.

Optimization of ECG Classification by Means of Feature Selection

This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.

QT variability and HRV interactions in ECG: quantification and reliability

In this paper, a dynamic linear approach was used over QT and RR series measured by an automatic delineator, to explore the interactions between QT interval variability (QTV) and heart rate variability (HRV). A low-order linear autoregressive model allowed to separate and quantify the QTV fractions correlated and not correlated with HRV, estimating their power spectral density measures. Simulated series and artificial ECG signals were used to assess the performance of the methods, considering a respiratory-like electrical axis rotation effect and noise contamination with a signal-to-noise ratio (SNR) from 30 to 10 dB. The errors found in the estimation of the QTV fraction related to HRV showed a nonrelevant performance decrease from automatic delineation. The joint performance of delineation plus variability analysis achieved less than 20% error in over 75% of cases for records presenting SNRs higher than 15 dB and QT standard deviation higher than 10 ms. The methods were also applied to real ECG records from healthy subjects where it was found a relevant QTV fraction not correlated with HRV (over 40% in 19 out of 23 segments analyzed), indicating that an important part of QTV is not linearly driven by HRV and may contain complementary information.

Evaluation of a wavelet-based ECG waveform detector on the QT database

We have evaluated a single-lead wavelet transform (WT) based detector of ECG significant points. A quadratic spline wavelet was used as prototype wavelet, and the first four scales of the Dyadic WT were analyzed. First of all, we detect QRS complexes. Then, the individual waves, the onset and the offset of the QRS complexes are identified and finally P and T peaks and their onset and offset are detected. We have validated the algorithm with the manual annotations in the QT Database (QTDB), developed for validation purposes. QRS and other ECG waveform boundaries were independently evaluated. The mean and standard deviation of the differences between the manual and detectors wave boundary annotations were calculated. The standard deviations obtained with the WT approach are around the accepted tolerances between expert physicians, outperforming the results of a low-pass differentiator algorithm, which was used as a reference, especially in the T wave offset. The QRS detector obtained a sensitivity of Se=99.91 and a positive predictivity of P+=99.88%.

Multilead Analysis of T-Wave Alternans in the ECG Using Principal Component Analysis

T-wave alternans (TWA) is a cardiac phenomenon associated with the mechanisms leading to sudden cardiac death. Several methods exist to automatically detect and estimate TWA in the ECG on a single-lead basis, and their main drawback is their poor sensitivity to low-amplitude TWA. In this paper, we propose a multilead analysis scheme to improve the detection and estimation of TWA. It combines principal component analysis with a single-lead method based on the generalized likelihood ratio test. The proposed scheme is evaluated and compared to a single-lead scheme by means of a simulation study, in which different types of simulated and physiological noise are considered under realistic conditions. Simulation results show that the multilead scheme can detect TWA with an SNR 30 dB lower and allows the estimation of TWA with an SNR 25 dB lower than the single-lead scheme. The two analysis schemes are also applied to stress test ECG records. Results show that the multilead scheme provides a higher detection power and that TWA detections obtained with this scheme are significantly different in healthy volunteers and ischemic patients, whereas they are not with the single-lead scheme.

An Automatic Patient-Adapted ECG Heartbeat Classifier Allowing Expert Assistance

In this paper, we present a patient-adaptable algorithm for ECG heartbeat classification, based on a previously developed automatic classifier and a clustering algorithm. Both classifier and clustering algorithms include features from the RR interval series and morphology descriptors calculated from the wavelet transform. Integrating the decisions of both classifiers, the presented algorithm can work either automatically or with several degrees of assistance. The algorithm was comprehensively evaluated in several ECG databases for comparison purposes. Even in the fully automatic mode, the algorithm slightly improved the performance figures of the original automatic classifier; just with less than two manually annotated heartbeats (MAHB) per recording, the algorithm obtained a mean improvement for all databases of 6.9% in accuracy A, of 6.5% in global sensitivity S and of 8.9% in global positive predictive value P+. An assistance of just 12 MAHB per recording resulted in a mean improvement of 13.1% in A, of 13.9% in S, and of 36.1% in P+. For the assisted mode, the algorithm outperformed other state-of-the-art classifiers with less expert annotation effort. The results presented in this paper represent an improvement in the field of automatic and patient-adaptable heartbeats classification, concluding that the performance of an automatic classifier can be improved with an efficient handling of the expert assistance.