PILOTING REAL-TIME QRS DETECTION ALGORITHMS IN VARIABLE CONTEXTS

This paper presents a cardiac arrhythmia medical monitoring system that can modify and change the components of its processing chain to carry out the best treatment on electrocardiogram (ECG) signal. The most important feature to detect in the ECG is the QRS complex. However, the experience gathered over several years, shows that the proposed strategies to detect the QRS complex have reached an asymptotic detection performance. We propose to use a mixture of low-level and high-level information, called the current context, to pilot QRS detection algorithms in order to reduce the number of errors. The algorithms are piloted according to a set of piloting rules acquired by statistical analysis. Results of piloting three QRS detectors on five test ECGs corrupted by real clinical noise, show that the pilot enables to reduce the error rate from 14,3% to 10,6%. These results are useful to the development of a real-time monitoring system which can choose the best algorithm to recognize arrhythmias in clinical noisy context. The presented approach is not restricted to the QRS complex detection but can be extended to the processing of other biomedical signals

Pilotage d'algorithmes pour un diagnostic médical robuste en cardiologie

Dans un environnement clinique, les systèmes de monitoring médical sont soumis à diverses sources de bruit qui conduisent à la détection d'informations non pertinentes voire erronées, et vont empêcher un diagnostic médical fiable. Pour répondre à ce problème, nous proposons d'intégrer un pilote d'algorithmes à un système de monitoring cardiaque. Grâce à l'analyse du bruit de ligne et du contexte pathologique (état du patient), le pilote modifie en ligne la ch aîne de traitement pour ne baser le diagnostic médical que sur des informations fiables (non bruitées) et strictement nécessaires. Pour valider notre approche nous avons testé le système avec des signaux pathologiques bruités typiques de situations cliniques. Les résultats de ces tests montrent l'intérêt et la faisabilité d'une telle approche

On-line apnea-bradycardia detection using hidden semi-Markov models.

International audienceIn this work, we propose a detection method that exploits not only the instantaneous values, but also the intrinsic dynamics of the RR series, for the detection of apnea-bradycardia episodes in preterm infants. A hidden semi-Markov model is proposed to represent and characterize the temporal evolution of observed RR series and different pre-processing methods of these series are investigated. This approach is quantitatively evaluated through synthetic and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU our best detector shows an improvement of around 13% in sensitivity and 7% in specificity. Furthermore, a reduced detection delay of approximately 3 seconds is obtained with respect to conventional detectors

Time-frequency relationships between heart rate and respiration: A diagnosis tool for late onset sepsis in sick premature infants

International audienceThe diagnosis of late onset sepsis in premature infants remains difficult because clinical signs are subtle and non-specific and none of the laboratory tests, including CRP and blood culture, have high predictive accuracy. Heart rate variability (HRV) analysis emerges as a promising diagnostic tool. Entropy and long-range fractal correlation are decreased in premature infants with proven sepsis. Besides this, respiration and its relations to HRV appear to be less. The objective of this study was to determine if analysis of time-frequency correlations between the heart rate and respiration amplitude may help for the diagnosis of infection in premature infants. An estimator of the linear relationship between nonstationary signals, recently introduced, is explored. The tests were performed on a cohort study of 60 premature infants. The results show that the correlation in the low frequency band tended to be higher in the sepsis group

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

A tissue-level model of the left ventricle for the analysis of regional myocardial function.

International audienceThis paper presents a model-based method for the analysis of regional myocardial strain, based on echocardiography and Tissue Doppler Imaging (TDI). A multi-formalism, tissue-level electromechanical model of the left ventricle is proposed. The parameters of the model are identified in order to reproduce regional strain signal morphologies obtained from a healthy subject and a patient presenting a dilated cardiomyopathy. The parameters identified for the DCM patient allow the localization of the failing myocardial segments and may be useful for a better design of cardiac resynchronization therapy on heart failure patients