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

Performance analysis of Hurst exponent estimators using surrogate-data and fractional lognormal noise models: Application to breathing signals from preterm infants

International audienceThe use of the Hurst exponent (H) to quantify the fractal characteristics of biological signals and its potential to detect abnormalities has aroused, recently, the interest of many researchers. Numerous techniques to estimate H are described in the literature, yet the choice of the most performing one is not straightforward. In this paper, we proposed some tests using artificial signals from experimental data and stochastic models to evaluate the robustness of three estimation techniques. Different surrogate-data tests, including a novel method to parametrize the degree of correlation in experimental signals with H (Hurst-adjusted surrogates), were first carried out. Then, simulated signals with prescribed H were obtained from fractional Gaussian noise modified properly to follow the lognormal laws observed in empirical data. The tests were applied to examine detrended fluctuation analysis (DFA), discrete wavelet transform and least squares based on standard deviation (LSSD) methods in the particular case of inter-breath interval signals from preterm infants. Simulations showed that none of the estimators were robust for every breathing pattern (regular, erratic and periodic) and should not be applied blindly without performing the preliminary tests proposed here. The LSSD technique was the most precise in general, but DFA was more robust with highly spiked patterns

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

Extraction des dynamiques du système nerveux autonome : Une approche basée sur la séparation aveugle de sources.

International audienceThis paper presents a method to estimate the Sympathetic and Parasympathetic Autonomic Nervous System components (SNAS and SNAP respectively) by using an indicator obtained from surface ECGs. The long-term goal of the project is to quantitatively analyze the ratio between SNAS and SNAP in order to better characterize certain pathologies. An approach based on Independent Component Analysis (ICA), which exploits the di erent activation delays of SNAS and SNAP is proposed and applied to a database with normal subjects and diabetic patients. Preliminary results show a good separation, in the frequency domain, of SNAS and SNAP dynamics, including a di erentiation of SNAS and SNAP in the low-frequency band. Results are promising for a better quanti cation of the autonomic state of the patients

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 T-wave Morphology Parameters in Drug-Induced Repolarization Abnormalities

International audienceThis study evaluates the predictive values of T-wave morphology parameters reflecting the repolarization changes by beat to beat calculation of parameters using different mathematical tools to identify additional markers sensitive to the variation induced by drug in the surface of the electrocardiogram. T-wave morphology indicators are extracted from nearly 6 hours recordings of two patients from a clinical d-Sotalol study. The results show that drug induced change in T-wave morphology as well as QT interval. In particular, parameters extracted from spherical coordinates of vectrocardiogram that were not tested before for drug effect evaluation show high sensitivity to small change induced by drugs

Modèle de connaissances pour l'interprétation d'un électrogramme

·Les électrogrammes (EGM) intracavitaires ne sont que très peu étudiés ou seulement d'un point de vue temporel avec l'analyse des relations entre les ondes détectées. Une nouvelle stratégie d'interprétation physiopathologique est proposée dans cet article. Elle associe de manière originale la modélisation de tissus cardiaques, la synthèse du signal EGM et l'estimation de paramètres à l'aide d'algorithmes évolutionnaires. Les résultats obtenus ont permis d'interpréter plusieurs types de comportements physiopathologiques

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