Evaluation of real-time QRS detection algorithms in variable contexts

http://www.iee.org/A method is presented to evaluate the detection performance of real-time QRS detection algorithms to propose a strategy for the adaptive selection of QRS detectors, under variable signal contexts. Signal contexts are defined as different combinations of QRS morphologies and clinical noise. Four QRS detectors are compared under these contexts by means of a multivariate analysis. This evaluation strategy is general and can be easily extended to a larger number of detectors. A set of morphology contexts, corresponding to 8 QRS morphologies (Normal, PVC, premature atrial beat, paced beat, LBBB, fusion, RBBB, junctional premature beat), has been extracted from 17 standard ECG records. For each morphology context, the set of extracted beats, ranging from 30 to 23000, are resampled to generate 50 realizations of 20 concatenated beats. These realizations are then used as input to the QRS detectors, without noise, and with 3 different types of additive clinical noise (electrode motion artefact, muscle artefact, baseline wander) at 3 signal-to-noise ratios (5dB, -5dB, -15dB). Performance is assessed by the number of errors, which reflects both false alarms and missed beats. The results show that the evaluated detectors are indeed complementary. For example, the Pan and Tompkins's detector is the best in most contexts but the Okada's detector generates less errors in presence of electrode motion artefact. These results will be particularly useful to the development of a real-time system that will be able to choose the best QRS detector according to the current context

Analysis of the high-frequency content in human qrs complexes by the continuous wavelet transform: An automatized analysis for the prediction of sudden cardiac death

Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk

Apprentissage d'arbre de décision pour le pilotage en ligne d'algorithmes de détection sur les électrocardiogrammes

National audienceLe nombre d'algorithmes de traitement du signal (compression, reconnaissance des formes, etc.) grandit progressivement ce qui rend de plus en plus difficile le choix de l'algorithme le plus adapté à une tâche particulière. Ceci est particulièrement vrai pour l'analyse automatique des électrocardiogrammes (ECG) notamment pour la détection des complexes QRS. Bien que chaque algorithme de la littérature se comporte de manière satisfaisante dans des situations normales, il existe des contextes où un algorithme est plus adapté que les autres, notamment en présence de bruit. Nous proposons une méthode de sélection qui choisit, en ligne, l'algorithme le plus adapté au contexte courant du signal à traiter. Les règles de sélection sont acquises par arbre de décision sur les résultats de performance de 7 algorithmes testés dans 130 contextes différents. Les résultats montrent la supériorité de l'approche proposée sur les algorithmes utilisés séparément. En outre, les performances des règles de sélection apprises sont très proches de celles des règles acquises par expertise, ce qui conforte notre approche

Relationship between body surface potential maps and atrial electrograms in patients with atrial fibrillation

PhD ThesisAtrial fibrillation (AF) is the most common cardiac arrhythmia. It is distinguished by fibrillating or trembling of the atrial muscle instead of normal contraction. Patients in AF have a much higher risk of stroke. AF is often driven by the left atrium (LA) and the diagnosis of AF is normally made from lead V1 in a 12-lead electrocardiogram (ECG). However, lead V1 is dominated by right atrial activity due to its proximal location to the right atrium (RA). Consequently it is not well understood how electrical activity from the LA contributes to the ECG. Studies of the AF mechanisms from the LA are typically based on invasive recording techniques. From a clinical point of view it is highly desirable to have an alternative, non-invasive characterisation of AF. The aim of this study was to investigate how the LA electrical activity was expressed on the body surface, and if it could be observed preferentially in different sites on the body surface. For this purpose, electrical activity of the heart from 20 patients in AF were recorded simultaneously using 64-lead body surface potential mapping (BSPM) and bipolar 10-electrode catheters located in the LA and coronary sinus (CS). Established AF characteristics such as amplitude, dominant frequency (DF) and spectral concentration (SC) were estimated and analysed. Furthermore, two novel AF characteristics (intracardiac DF power distribution, and body surface spectral peak type) were proposed to investigate the relationship between the BSPM and electrogram (EGM) recordings. The results showed that although in individual patients there were body surface sites that preferentially represented the AF characteristics estimated from the LA, those sites were not consistent across all patients. It was found that the left atrial activity could be detected in all body surface sites such that all sites had a dominant or non-dominant spectral peak corresponding to EGM DF. However, overall the results suggested that body surface site 22 (close to lead V1) was more closely representative of the CS activity, and site 49 (close to the posterior lower central right) was more closely representative of the left atrial activity. There was evidence of more accurate estimation of AF characteristics using additional electrodes to lead V1