11 research outputs found
Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine
This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings
Noise-Assisted Instantaneous Coherence Analysis of Brain Connectivity
Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency
representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power
spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly
computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is
designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from
visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data
Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on
approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states
Multiscale entropy analysis of heart rate variability in neonatal patients with and without seizures
The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns
Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine
This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings
Multivariate Multiscale Dispersion Entropy of Biomedical Times Series
Due to the non-linearity of numerous physiological recordings, non-linear analysis of multi-channel signals has been extensively used in biomedical engineering and neuroscience. Multivariate multiscale sample entropy (MSE–mvMSE) is a popular non-linear metric to quantify the irregularity of multi-channel time series. However, mvMSE has two main drawbacks:
(1) the entropy values obtained by the original algorithm of mvMSE are either undefined or unreliable for short signals (300 sample points); and
(2) the computation of mvMSE for signals with a large number of channels requires the storage of a huge number of elements.
To deal with these problems and improve the stability of mvMSE, we introduce multivariate multiscale dispersion entropy (MDE–mvMDE), as an extension of our recently developed MDE, to quantify the complexity of multivariate time series. We assess mvMDE, in comparison with the state-of-the-art and most widespread multivariate approaches, namely, mvMSE and multivariate multiscale fuzzy entropy (mvMFE), on multi-channel noise signals, bivariate autoregressive processes, and three biomedical datasets. The results show that mvMDE takes into account dependencies in patterns across both the time and spatial domains. The mvMDE, mvMSE, and mvMFE methods are consistent in that they lead to similar conclusions about the underlying physiological conditions.
However, the proposed mvMDE discriminates various physiological states of the biomedical recordings better than mvMSE and mvMFE. In addition, for both the short and long time series, the mvMDE-based results are noticeably more stable than the mvMSE- and mvMFE-based ones. For short multivariate time series, mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is faster than mvMFE and mvMSE and also needs to store a considerably smaller number of elements. Due to its ability to detect different kinds of dynamics of multivariate signals, mvMDE has great potential to analyse various signals
Perceptual Constraints and the Dynamics of Movement Execution and Learning
Guidance by simple visual patterns has been reported to facilitate performance of difficult coordination patterns. This kind of guidance, however, might significantly alter coordination dynamics and learning. Experiment 1 investigated the effect of visual guidance on the organization of bimanual coordination. Anti-phase 1:1 was performed without (i) augmented information, (ii) under metronome pacing, and (iii) under visual guidance by a Lissajous plot. DFA analysis revealed that the temporal dynamics of amplitudes and relative phase values deviated from the typical 1/f variation towards more random variation under visual guidance. Complexity of amplitudes, periods and relative phases, as measured by multiscale entropy, were also lowered in visual guidance. Experiment 2 investigated whether the dynamical effects visual guidance have any role in learning. Specifically, the effects of practicing bimanual coordination at 90° of relative phase with constant visual guidance by a Lissajous plot, a fading schedule of guidance and no guidance were investigated. After practice, individuals were tested in independent execution (with no guidance) and under visual guidance. Practice conditions did not affect temporal correlation of phases, amplitudes of periods at final tests. Complexity of amplitudes and periods showed some increase in the no guidance test for the group that practiced under constant visual guidance, but not for the other groups. A specificity of practice effect on complexity was found: performance in the visually guided test was associated with a general decrease in complexity for all groups (replicating Experiment 1), except for participants that practiced with constant visual guidance
Data-driven time-frequency analysis of multivariate data
Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition
and time-frequency analysis of real world nonstationary signals. Its main advantages over
other time-frequency methods are its locality, data-driven nature, multiresolution-based
decomposition, higher time-frequency resolution and its ability to capture oscillation of
any type (nonharmonic signals). These properties have made EMD a viable tool for real
world nonstationary data analysis.
Recent advances in sensor and data acquisition technologies have brought to light
new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore,
imperative to design multivariate extensions of the existing nonlinear and nonstationary
analysis algorithms as they are expected to give more insight into the dynamics and the
interdependence between multiple channels of such signals.
To this end, this thesis presents multivariate extensions of the empirical mode de-
composition algorithm and illustrates their advantages with regards to multivariate non-
stationary data analysis. Some important properties of such extensions are also explored,
including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple
data channels. Owing to the generality of the proposed methods, an improved multi-
variate EMD-based algorithm is introduced which solves some inherent problems in the
original EMD algorithm. Finally, to demonstrate the potential of the proposed methods,
simulations on the fusion of multiple real world signals (wind, images and inertial body
motion data) support the analysis
Multivariate multiscale complexity analysis
Established dynamical complexity analysis measures operate at a single scale and thus fail
to quantify inherent long-range correlations in real world data, a key feature of complex
systems. They are designed for scalar time series, however, multivariate observations are
common in modern real world scenarios and their simultaneous analysis is a prerequisite for
the understanding of the underlying signal generating model. To that end, this thesis first
introduces a notion of multivariate sample entropy and thus extends the current univariate
complexity analysis to the multivariate case. The proposed multivariate multiscale entropy
(MMSE) algorithm is shown to be capable of addressing the dynamical complexity of such
data directly in the domain where they reside, and at multiple temporal scales, thus
making full use of all the available information, both within and across the multiple data
channels. Next, the intrinsic multivariate scales of the input data are generated adaptively
via the multivariate empirical mode decomposition (MEMD) algorithm. This allows for
both generating comparable scales from multiple data channels, and for temporal scales
of same length as the length of input signal, thus, removing the critical limitation on
input data length in current complexity analysis methods. The resulting MEMD-enhanced
MMSE method is also shown to be suitable for non-stationary multivariate data analysis
owing to the data-driven nature of MEMD algorithm, as non-stationarity is the biggest
obstacle for meaningful complexity analysis. This thesis presents a quantum step forward
in this area, by introducing robust and physically meaningful complexity estimates of
real-world systems, which are typically multivariate, finite in duration, and of noisy and
heterogeneous natures. This also allows us to gain better understanding of the complexity
of the underlying multivariate model and more degrees of freedom and rigor in the analysis.
Simulations on both synthetic and real world multivariate data sets support the analysis
Entropy-based nonlinear analysis for electrophysiological recordings of brain activity in Alzheimer’s disease
Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain
cells causes memory loss and cognitive decline. As AD progresses, changes in the
electrophysiological brain activity take place. Such changes can be recorded by the
electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are
the only two neurophysiologic approaches able to directly measure the activity of the brain
cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e.,
brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs.
One of the most powerful nonlinear metrics used to assess the dynamical characteristics of
signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for
characterization of EEGs and MEGs paying close attention to AD. Recent developments in the
field of entropy for the characterization of physiological signals have tried: 1) to improve the
stability and reliability of entropy-based results for short and long signals; and 2) to extend the
univariate entropy methods to their multivariate cases to be able to reveal the patterns across
channels.
To enhance the stability of entropy-based values for short univariate signals, refined composite
multiscale fuzzy entropy (MFE - RCMFE) is developed. To decrease the running time and
increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the
refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed
here as well.
In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn),
RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast
enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn)
and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns
and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and
FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular
sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of
equal values for embedded vectors and discarding some information with regard to the signal
amplitudes encountered in permutation entropy (PerEn). Moreover, unlike PerEn, DispEn and
FDispEn are relatively insensitive to noise.
As extensions of our developed DispEn, multiscale DispEn (MDE) and multivariate MDE
(mvMDE) are introduced to quantify the complexity of univariate and multivariate signals,
respectively. MDE and mvMDE have the following advantages over the existing univariate
and multivariate multiscale methods: 1) they are noticeably faster; 2) MDE and mvMDE result
in smaller coefficient of variations for synthetic and real signals showing more stable profiles;
3) they better distinguish various states of biomedical signals; 4) MDE and mvMDE do not
result in undefined values for short time series; and 5) mvMDE, compared with multivariate
multiscale SampEn (mvMSE) and mvMFE, needs to store a considerably smaller number of
elements.
In this Thesis, two restating-state electrophysiological datasets related to AD are analyzed: 1)
148-channel MEGs recorded from 62 subjects (36 AD patients vs. 26 age-matched controls);
and 2) 16-channel EEGs recorded from 22 subjects (11 AD patients vs. 11 age-matched
controls). The results obtained by MDE and mvMDE suggest that the controls’ signals are
more and less complex at respectively short (scales between 1 to 4) and longer (scales between
5 to 12) scale factors than AD patients’ recordings for both the EEG and MEG datasets. The
p-values based on Mann-Whitney U-test for AD patients vs. controls show that the MDE
and mvMDE, compared with the existing complexity techniques, significantly discriminate
the controls from subjects with AD at a larger number of scale factors for both the EEG and
MEG datasets. Moreover, the smallest p-values are achieved by MDE (e.g., 0.0010 and 0.0181
for respectively MDE and MFE using EEG dataset) and mvMDE (e.g., 0.0086 and 0.2372 for
respectively mvMDE and mvMFE using EEG dataset) for both the EEG and MEG datasets,
illustrating the superiority of these developed entropy-based techniques over the state-of-the-art
univariate and multivariate entropy approaches.
Overall, the introduced FDispEn, DispEn, MDE, and mvMDE methods are expected to be
useful for the analysis of physiological signals due to their ability to distinguish different types
of time series with a low computation time