Adaptive frequency tracking and application to biomedical signals

The main information of a signal resides in its frequency, its amplitude (or its power) and in their temporal evolution. Thus, a great number of methods for instantaneous frequency estimation have been proposed in the literature. Most of these algorithms use an adaptive filter-based (notch or bandpass filter) structure. In many applications, the information of interest is present in more than one signal. However, to our knowledge no algorithm able to track the frequency on several signals has been presented in the literature. Usually, frequency components are estimated and extracted on each signal independently. Artifacts or noise relative to a specific signal can thus disturb the frequency estimation process. Moreover, low amplitude components present in every signal (but non dominant) will not be estimated. The objective in the first part of this thesis is to develop different methods able to extend existing frequency tracking algorithm in order to improve the quality of the estimate, in terms of estimation variance and robustness with respect to noise. The proposed methods can be applied to algorithms using adaptive filters for the frequency estimation. For the multi-signal frequency tracking extension, these methods use the redundancy of information present in the signals under study. A first approach uses a unique filter for every signal. A set of weights is computed, depending on a measure of the estimate quality, and makes it possible to balance the influence of each signal on the tracking filter update. The second approach consists in using a different adaptive filter for each signal. A set of constraints links the central frequencies of each filter so that they are as similar as possible. Both methods yield frequency estimates more robust with respect to noise and more stable, without any decrease in estimation accuracy. For the harmonic frequency tracking, we propose a method using the information present in the harmonic component to improve the estimate of the fundamental frequency. The proposed methods also permit to extract the signal components corresponding to the estimated frequencies. These components are very useful for subsequent study. In the second part of this thesis, the algorithms developed in the first part are applied to biomedical signals. Two different applications are studied in this work : electrocardiograms and electroencephalograms. Firstly, a frequency tracking algorithm as well as its multi-signal extension are used to predict the success of electrical cardioversion attempts in patients suffering from atrial fibrillation. The instantaneous frequency is estimated using the algorithms and the corresponding signal component is extracted from electrocardiograms recorded prior to the attempt. With a few parameters computed on the estimated frequency and the corresponding signal component, we were able to predict the result of the cardioversion attempt on our database comprising 18 patients with a success rate of 94% for both algorithms. We think that this result can be very useful for helping the clinician to choose the appropriate therapy for atrial fibrillation management. The developed algorithms are also used to track the oscillatory components present in electroencephalograms. The performance of the basic algorithm is illustrated using single-trial electroencephalogram signals from a visual evoked potential experiment. The algorithm is used to track the gamma component (30-50 Hz). It is able to successfully track the spectral component in spite of the fact that large amplitude variations are present in the signal. A complex version of the multi-signal extension is also used to have an algorithm able to track multiple frequency components on multiple signals. The performance of this algorithm is also illustrated with single-trial electroencephalogram signals. It was shown to be able to correctly track up to four frequency components simultaneously. The quality of the estimation is improved using multiple lead signals

Nonlinear behaviour of conduction and block in cardiac tissue with heterogeneous expression of connexin 43

Altered gap junctional coupling potentiates slow conduction and arrhythmias. To better understand how heterogeneous connexin expression affects conduction at the cellular scale, we investigated conduction in tissue consisting of two cardiomyocyte populations expressing different connexin levels. Conduction was mapped using microelectrode arrays in cultured strands of foetal murine ventricular myocytes with prede fi ned contents of connexin 43 knockout (Cx43KO) cells. Corresponding computer simulations were run in randomly generated two-dimensional tissues mimicking the cellular architecture of the strands. In the cultures, the relationship between conduction velocity (CV) and Cx43KO cell content was nonlinear. CV fi rst decreased signi fi cantly when Cx43KO content was increased from 0 to 50%. When the Cx43KO content was ≥ 60%, CV became comparabletothatin100%Cx43KOstrands.Co-culturingCx43KOandwild-typecellsalsoresultedinsigni fi cantly more heterogeneous conduction patterns and in frequent conduction blocks. The simulations replicated this behaviour of conduction. For Cx43KO contents of 10 – 50%, conduction was slowed due to wavefront meandering between Cx43KO cells. For Cx43KO contents ≥ 60%, clusters of remaining wild-type cells acted as electrical loads thatimpairedconduction.ForCx43KOcontentsof40 – 60%,conductionexhibitedfractal characteristics,wasprone to block, and was more sensitive to changes in ion currents compared to homogeneous tissue. In conclusion, conduction velocity and stability behave in a nonline ar manner when cardiomyocytes expressing different connexin amounts are combined. This behaviour results from heterogeneous current-to-load relationships at the cellular level. Such behaviour is likely to be arrhythmogenic in various clinical contexts in which gap junctional coupling is heterogeneous

Adaptive Frequency Tracking on the ECG Used to Predict the Success of Electrical Cardioversion of Atrial Fibrillation

One of the rhythm control strategies in atrial fibrillation management consists in the application of a brief single electrical shock to the atria. This electrical cardioversion does not always succeed, and the long-term success of this therapy is not sufficiently predictable on the basis of clinical and echocardiographic parameters. In noninvasive atrial fibrillation studies, the frequencies observed in the atrial ECG signals are considered as an indicator of the underlying dynamics. In this study, we examen the use of frequency tracking techniques in predicting the success of electrical cardioversion

Adaptive Tracking of EEG Frequency Components

In this chapter, we propose a novel method for tracking oscillatory components in EEG signals by means of an adaptive filter-bank. The specificity of our tracking algorithm is to maximize the oscillatory behavior of its output rather than its spectral power, which shows interesting properties for the observation of neuronal oscillations. Besides, the structure of the filter-bank allows for efficiently tracking multiple frequency components perturbed by noise, therefore providing a good framework for EEG spectral analysis. Moreover, our algorithm can be generalized to multivariate data analysis, allowing the simultaneous investigation of several EEG sensors. Thus, a more precise extraction of spectral information can be obtained from the EEG signal under study. After a short introduction, we present our algorithm as well as synthetic examples illustrating its potential. Then, the performance of the method on real EEG signals is presented for the tracking of both single oscillatory component and multiple components. Finally, future lines of improvement as well as areas of applications are discussed

Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N 20) or long-standing AF (N 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9 were obtained, with a predictive value for long-standing AF of 93.1. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings

Stochastic pacing reveals the propensity to cardiac action potential alternans and uncovers its underlying dynamics

KEY POINTS: Beat‐to‐beat alternation (alternans) of the cardiac action potential duration is known to precipitate life‐threatening arrhythmias and can be driven by the kinetics of voltage‐gated membrane currents or by instabilities in intracellular calcium fluxes. To prevent alternans and associated arrhythmias, suitable markers must be developed to quantify the susceptibility to alternans; previous theoretical studies showed that the eigenvalue of the alternating eigenmode represents an ideal marker of alternans. Using rabbit ventricular myocytes, we show that this eigenvalue can be estimated in practice by pacing these cells at intervals varying stochastically. We also show that stochastic pacing permits the estimation of further markers distinguishing between voltage‐driven and calcium‐driven alternans. Our study opens the perspective to use stochastic pacing during clinical investigations and in patients with implanted pacing devices to determine the susceptibility to, and the type of alternans, which are both important to guide preventive or therapeutic measures. ABSTRACT: Alternans of the cardiac action potential (AP) duration (APD) is a well‐known arrhythmogenic mechanism. APD depends on several preceding diastolic intervals (DIs) and APDs, which complicates the prediction of alternans. Previous theoretical studies pinpointed a marker called λ(alt) that directly quantifies how an alternating perturbation persists over successive APs. When the propensity to alternans increases, λ(alt) decreases from 0 to –1. Our aim was to quantify λ(alt) experimentally using stochastic pacing and to examine whether stochastic pacing allows discriminating between voltage‐driven and Ca(2+)‐driven alternans. APs were recorded in rabbit ventricular myocytes paced at cycle lengths (CLs) decreasing progressively and incorporating stochastic variations. Fitting APD with a function of two previous APDs and CLs permitted us to estimate λ(alt) along with additional markers characterizing whether the dependence of APD on previous DIs or CLs is strong (typical for voltage‐driven alternans) or weak (Ca(2+)‐driven alternans). During the recordings, λ(alt) gradually decreased from around 0 towards –1. Intermittent alternans appeared when λ(alt) reached –0.8 and was followed by sustained alternans. The additional markers detected that alternans was Ca(2+) driven in control experiments and voltage driven in the presence of ryanodine. This distinction could be made even before alternans was manifest (specificity/sensitivity >80% for –0.4 > λ(alt) > –0.5). These observations were confirmed in a mathematical model of a rabbit ventricular myocyte. In conclusion, stochastic pacing allows the practical estimation of λ(alt) to reveal the onset of alternans and distinguishes between voltage‐driven and Ca(2+)‐driven mechanisms, which is important since these two mechanisms may precipitate arrhythmias in different manners