Abstract This paper considers the problem of change-point detection for noisy data. Estimation of signal frequency content relies on differential algebra and non-commutative algebra together with operational calculus. We adapt this approach to the study of changes that may be observed in EEG signal dynamics during epileptic seizure and in ECG signal during the occurrence of a QRS complex. The correlation with frequency change is what this idea is based on. The interest of our estimator is firstly illustrated according to several academic examples. Then, the method is applied on real physiological signals to detect abrupt frequency changes
Recognition of a change of the signal properties is important task in various fields of an EEG analy...
Time-varying time-frequency complexity measures for epileptic EEG data analysis</p
In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected dur...
International audienceThis paper considers the problem of change-point detection for noisy data. Est...
We investigated change point detection (CPD) in time series composed of harmonic functions driven by...
This paper describes adaptive time frequency analysis of EEG signals, both in theory as well as in p...
This paper considers the general problem of detecting change in non-stationary signals using feature...
Change point detection is a critical analysis in various scientific fields such as finance, medicine...
We present a new, robust, model-independent technique for measuring condition change in nonlinear da...
The scientic framework of this thesis is the modeling of electrophysiologicals signals bymodern tool...
The goal in this article is to develop a practical tool that identifies changes in the brain activit...
The main information of a signal resides in its frequency, its amplitude (or its power) and in their...
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in n...
We consider the problem of detecting a change in an arbitrary vector process by examining the evolut...
Abstract: In this paper we consider the stationary level of electroencephalogram time seri...
Recognition of a change of the signal properties is important task in various fields of an EEG analy...
Time-varying time-frequency complexity measures for epileptic EEG data analysis</p
In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected dur...
International audienceThis paper considers the problem of change-point detection for noisy data. Est...
We investigated change point detection (CPD) in time series composed of harmonic functions driven by...
This paper describes adaptive time frequency analysis of EEG signals, both in theory as well as in p...
This paper considers the general problem of detecting change in non-stationary signals using feature...
Change point detection is a critical analysis in various scientific fields such as finance, medicine...
We present a new, robust, model-independent technique for measuring condition change in nonlinear da...
The scientic framework of this thesis is the modeling of electrophysiologicals signals bymodern tool...
The goal in this article is to develop a practical tool that identifies changes in the brain activit...
The main information of a signal resides in its frequency, its amplitude (or its power) and in their...
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in n...
We consider the problem of detecting a change in an arbitrary vector process by examining the evolut...
Abstract: In this paper we consider the stationary level of electroencephalogram time seri...
Recognition of a change of the signal properties is important task in various fields of an EEG analy...
Time-varying time-frequency complexity measures for epileptic EEG data analysis</p
In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected dur...