Estimating the real-time respiratory rate from the ECG with a bank of notch filters

The respiratory rate is an important vital sign that needs to be monitored continuously in clinical and non-clinical health monitoring applications. It is commonly estimated from electrocardiogram (ECG)-derived respiratory waveforms such as the respiratory sinus arrhythmia~(RSA) and the ECG R peak amplitude~(RPA). Current methods combine respiratory information from these two waveforms but produce large delays in estimating the respiratory rate. In this work, the power of the outputs of a bank of order-3 FIR notch filters were used in an adaptive scheme to estimate in a real-time manner and with minimal delay the respiratory rate from the RSA and the RPA waveforms simultaneously. The algorithm was tested on the public Physionet Fantasia data set and compared to the state-of-the-art in terms of estimation accuracy and delay. It was shown that the proposed method provides more accurate estimates with smaller delays than those of the state-of-the-art

Linear regression model selection using a simplex reproduction genetic algorithm

Dans cet article on propose une nouvelle méthode de sélection de modèles linéairement regressifs. Un algorithme génétique (AG) basé sur une petite population est introduit. Le codage simple des paramètres et la convergence rapide rend la complexité modeste. La première application montre comme des modèles autoregressifs et des modèles polynomiaux nonlinéaires peuvent être correctement selectionnés. La complexité est donnée en fonction du nombre de générations et du nombre d'évaluations nécessaires jusqu'à la convergence de l'algorithme. Ensuite, l'AG est appliqué en tant qu'algorithme d'entraînement pour des réseaux à fonctions radiales de base. La stratégie de sélection des centres est comparée à l'algorithme d'entraînement par moindres carrés. Pour des sous-ensembles à diffrentes dimensions, l'AG fournit de meilleurs modèles que l'algorithme des moindres carrés

Respiratory Rate Estimation from Multi-Lead ECGs using an Adaptive Frequency Tracking Algorithm

Estimating the respiratory rate (RR) from the electrocardiogram (ECG) is of interest as the direct measurement of the respiration in clinical situations is often cumbersome. In this study, the RR was estimated from the multi-lead ECG R-peak amplitude (RPA) waveforms, which contain the modulation of the cardiac activity by the respiration. An adaptive oscillator-based frequency tracking algorithm was used to estimate the RR from the RPAs of two or three ECG leads. This automatic and instantaneous method tracks the common respiratory frequency which is present in its inputs as the RR estimate. On a subset of the Physionet MFH/MF dataset, it was shown that combining information from three leads yielded more accurate RR estimates than using two leads or each lead alone. It was also shown that the frequency tracking algorithm outperformed Fourier-based frequency estimation

Local dynamical analysis of the invariant set of a signal

Bien que l'analyse globale des systèmes nonlinéaires soit beaucoup utilisée, peu d'essais ont été faits pour tenter de charactériser leur comportement local, et les succès obtenus furent limités. Ceci provient principalement de ce que ces tentatives se sont concentrées sur l'estimation quantitative du comportement, et que les zones d'intérêt ont été déterminées de manière assez arbitraire. Cet article se propose de résoudre le problème en présentant une technique estimant l'exposant de Lyapunov local pour tous les points de l'attracteur et les agglomérant en groupes significatifs. Les exemples basés sur le système de Lorenz montrent que, si les résultats sont souvent peu fiables numériquement, ils permettent une analyse qualitative de 1' attracteur, et les groupes formés ont une signification physique

Estimating the time scale and anatomical location of atrial fibrillation spontaneous termination in a biophysical model

Due to their transient nature, spontaneous terminations of atrial fibrillation (AF) are difficult to investigate. Apparently, confounding experimental findings about the time scale of this phenomenon have been reported, with values ranging from 1s to 1min. We propose a biophysical modeling approach to study the mechanisms of spontaneous termination in two models of AF with different levels of dynamical complexity. 8s preceding spontaneous terminations were studied and the evolution of cycle length and wavefront propagation were documented to assess the time scale and anatomical location of the phenomenon. Results suggest that termination mechanisms are dependent on the underlying complexity of AF. During simulated AF of low complexity, the total process of spontaneous termination lasted 3,200ms and was triggered in the left atrium 800ms earlier than in the right atrium. The last fibrillatory activity was observed more often in the right atrium. These asymmetric termination mechanisms in both time and space were not observed during spontaneous terminations of complex AF simulations, which showed less predictable termination patterns lasting only 1,600ms. This study contributes to the interpretation of previous clinical observations, and illustrates how computer modeling provides a complementary approach to study the mechanisms of cardiac arrhythmia

Extraction of audio features specific to speech production for multimodal speaker detection

A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an MI-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdf) of the features, nor for the cost function itself. The pdf are estimated from the samples using a non-parametric approach. The challenging optimization problem is solved using a global method: the Differential Evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech is discussed. Using these specific speech audio features, candidates video features are then classified as membership of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on home- grown test sequences, and of 85% on most commonly used sequences