Efficient Schemes for Adaptive Frequency Tracking and their Relevance for EEG and ECG

Amplitude and frequency are the two primary features of one-dimensional signals, and thus both are widely utilized to analysis data in numerous fields. While amplitude can be examined directly, frequency requires more elaborate approaches, except in the simplest cases. Consequently, a large number of techniques have been proposed over the years to retrieve information about frequency. The most famous method is probably power spectral density estimation. However, this approach is limited to stationary signals since the temporal information is lost. Time-frequency approaches were developed to tackle the problem of frequency estimation in non-stationary data. Although they can estimate the power of a signal in a given time interval and in a given frequency band, these tools have two drawbacks that make them less valuable in certain situations. First, due to their interdependent time and frequency resolutions, improving the accuracy in one domain means decreasing it in the other one. Second, it is difficult to use this kind of approach to estimate the instantaneous frequency of a specific oscillatory component. A solution to these two limitations is provided by adaptive frequency tracking algorithms. Typically, these algorithms use a time-varying filter (a band-pass or notch filter in most cases) to extract an oscillation, and an adaptive mechanism to estimate its instantaneous frequency. The main objective of the first part of the present thesis is to develop such a scheme for adaptive frequency tracking, the single frequency tracker. This algorithm compares favorably with existing methods for frequency tracking in terms of bias, variance and convergence speed. The most distinguishing feature of this adaptive algorithm is that it maximizes the oscillatory behavior at its output. Furthermore, due to its specific time-varying band-pass filter, it does not introduce any distortion in the extracted component. This scheme is also extended to tackle certain situations, namely the presence of several oscillations in a single signal, the related issue of harmonic components, and the availability of more than one signal with the oscillation of interest. The first extension is aimed at tracking several components simultaneously. The basic idea is to use one tracker to estimate the instantaneous frequency of each oscillation. The second extension uses the additional information contained in several signals to achieve better overall performance. Specifically, it computes separately instantaneous frequency estimates for all available signals which are then combined with weights minimizing the estimation variance. The third extension, which is based on an idea similar to the first one and uses the same weighting procedure as the second one, takes into account the harmonic structure of a signal to improve the estimation performance. A non-causal iterative method for offline processing is also developed in order to enhance an initial frequency trajectory by using future information in addition to past information. Like the single frequency tracker, this method aims at maximizing the oscillatory behavior at the output. Any approach can be used to obtain the initial trajectory. In the second part of this dissertation, the schemes for adaptive frequency tracking developed in the first part are applied to electroencephalographic and electrcardiographic data. In a first study, the single frequency tracker is used to analyze interactions between neuronal oscillations in different frequency bands, known as cross-frequency couplings, during a visual evoked potential experiment with illusory contour stimuli. With this adaptive approach ensuring that meaningful phase information is extracted, the differences in coupling strength between stimuli with and without illusory contours are more clearly highlighted than with traditional methods based on predefined filter-banks. In addition, the adaptive scheme leads to the detection of differences in instantaneous frequency. In a second study, two organization measures are derived from the harmonic extension. They are based on the power repartition in the frequency domain for the first one and on the phase relation between harmonic components for the second one. These measures, computed from the surface electrocardiogram, are shown to help predicting the outcome of catheter ablation of persistent atrial fibrillation. The proposed adaptive frequency tracking schemes are also applied to signals recorded in the field of sport sciences in order to illustrate their potential uses. To summarize, the present thesis introduces several algorithms for adaptive frequency tracking. These algorithms are presented in full detail and they are then applied to practical situations. In particular, they are shown to improve the detection of coupling mechanisms in brain activity and to provide relevant organization measures for atrial fibrillation.

Adaptive frequency tracking and application to biomedical signals

Yann Prudat

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.

EPFL2009

Adaptive tracking of EEG oscillations

Cédric Duchene, Yann Prudat, Laurent Renaud Uldry, Jérôme Sébastien Van Zaen, Jean-Marc Vesin

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

2010

Multi-signal extension of adaptive frequency tracking algorithms

Yann Prudat, Jean-Marc Vesin

Adaptive tracking of sinusoidal signal components with time-varying amplitudes and frequencies presents a great interest in many engineering applications. In many cases, the frequency components of interest are present in more than one signal. So we propose, in this paper, a simple but efficient approach to extend existing algorithms to track frequency components simultaneously in several signals. Computer simulations and experiments on real signals demonstrate the potential of this approach in terms of estimation variance, convergence speed, and the capability to extract a frequency component common to several signals. (c) 2008 Elsevier B.V. All rights reserved.

2009

Adaptive frequency tracking and application to biomedical signals

Yann Prudat

EPFL2009