390 research outputs found
Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals
Full text linkObjective: We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications.Methods: We introduce multiscale dispersion entropy (DisEn - MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N^2) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE.Results: We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE.Conclusion: For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings.Significance: MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The Matlab codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/1982
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Mr Secrets and Social Media: the Confession of Richard Rodriguez
Full text linkRichard Rodriguez's works create troubling situations for many scholars. Though numerous critics see him as the penultimate Chicano writer, many others see his writing as only pandering to the elite. However, all politics and controversies aside, he is a writer whose ideas upon language and public confession have been revolutionary. Throughout the thesis, I argue that Rodriguez's ideas upon language and identity are applicable to the social media landscape that we reside in currently, especially the public confession. Also, I use deconstructionism, along with postmodern criticism, to illustrate the changing arc of Rodriguez's confession from his first autobiography to his final one. In his first memoir, Rodriguez remains in the closet upon his sexuality, and the reader only catches glimpses of the 'real' character inside his work. In the second memoir, the reader sees a better glimpse because of his coming out; yet, even in this regard, he does not do so wholly and still leaves his confession unfinished. By the third, he applies themes and problems seen in his first and second works to discuss our browning nature, and how we are all sinners and that we desire to confess our sins. In my assessment of Rodriguez, I argue throughout all my chapters upon a measure of irreconcilability between the private world of the Hispanic immigrant family and the public sphere that they are forced to inhabit because of his citizenship and education. This irreconcilability creates a drastic limiting of identity for the author that Rodriguez is forced to navigate which creates his desire for confession
La biología del suelo en sistemas agroecológicos
No full textDesde hace tiempo es notorio, especialmente para nosotros como investigadores de la biología del suelo, un creciente interés por parte de la sociedad en general, y en particular por las personas relacionadas con la agricultura y la ganadería, sobre la biología del suelo. La biología, la biota, la biodiversidad del suelo, son términos muy utilizados en ámbitos...Fil: Bedano, José Camilo. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; ArgentinaFil: Domínguez, Anahí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; ArgentinaFil: Rodriguez, Maria Pia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; ArgentinaFil: Ortiz, Carolina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; ArgentinaFil: Escudero, Héctor Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentin
Higher-order tensor decompositions for muscle synergy analysis
Full text linkThis doctoral thesis outlines several methodological advances in the application of higher-order
tensor decomposition for muscle synergy analysis estimated from surface Electromyogram
(EMG). This entails both assessing current muscle synergy extraction methods and a novel
direct approach to estimate useful muscle synergies using higher-order tensor decomposition.
The underlying hypothesis is that higher-order tensor decompositions provide advantages in
the estimation of temporal profiles and muscle synergies thanks to the consideration of other
domains such as spectral, task or repetition information. Moreover, we implement these
advances to inspect potential applications of tensor synergies in biomechanical analysis and
myoelectric control.
Firstly, we provide an overview of the current mathematical models for the concept of
muscle synergies and compare the common matrix factorisation methods for muscle synergy
extraction, in addition to second-order blind identification (SOBI), a technique which has not
been used for muscle synergy estimation previously. Synthetic and real EMG datasets related
to wrist movements from the publicly available Ninapro dataset were used in this evaluation.
Results suggest that a sparse synergy model and a higher number of channels would result in
better-estimated synergies. SOBI has better performance when a limited number of electrodes
is available, but its performance is still poor in that case. Overall, non-negative matrix
factorisation (NMF) is the most appropriate method for synergy extraction and, therefore, it is
considered as a benchmark in the rest of the thesis.
We then show the benefits of higher-order tensor decompositions of EMG data for muscle
synergy analysis, discussing possible 3rd and 4th-order tensors models for EMG data. We
explore muscle synergy estimation from 4th-order EMG tensors by taking the spectral
profile into account and utilise this model for classification between the wrist’s movements
in comparison with NMF. The results provide a proof-of-concept for higher-order tensor
decomposition as classification accuracy is slightly improved using tensor decomposition over
NMF. However, the addition of spectral mode -with time-frequency analysis- increases the
computational cost for tensor synergy estimation.
After the previous proof of concept, we focus on the 3rd -order tensor model for efficient and
reliable extraction of meaningful muscle synergies. The most prominent tensor decomposition
models (Tucker and PARAFAC) are compared under different constraints. We notice that
unconstrained Tucker decomposition cannot extract unique and consistent muscle synergies
as it converges into different local minima, while PARAFAC model cannot deal with a
higher number of synergies or tasks as the decomposition deviates from the trilinear model.
As a result, we introduce a constrained Tucker decomposition model as a framework for
muscle synergy analysis. The advantages of this method over NMF are highlighted in the
biomechanical application of identifying shared and task-specific muscle synergies. This
benefits from the natural multi-way form of the EMG data, which makes higher-order tensor
decompositions a better option than applying matrix factorisation repetitively. The constrained
Tucker decomposition can successfully identify shared and task-specific synergies and is robust
to disarrangement regarding task-repetition information, unlike NMF.
The constrained Tucker model is then used as a framework to extract synergistic information
that could be applied to proportional upper limb myoelectric control. The consistency of
extracted muscle synergies with the increase of the wrist’s task dimensionality into 3 degrees
of freedom (DoF) is investigated in comparison with NMF. In the literature, NMF approaches
for synergy-based proportional myoelectric control were viable only with a task dimension of 2
DoF. In contrast, the results show that a constrained Tucker model identifies consistent muscle
synergies from 3-DoFs dataset directly. Moreover, a tensor-based approach for proportional
myoelectric control is introduced and compared against NMF and sparse NMF as state of the
art benchmarks.
To sum up, higher-order tensor decomposition had not been utilised in EMG analysis despite
the substantial attention it received in biomedical signal processing applications in recent years.
This thesis explores higher-order tensor decompositions for synergy extraction to account for
the natural multi-way structure of EMG data. We hope that it will pave the way for the
development of muscle activity analysis methods based on higher-order techniques in broader
applications
Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings
Full text linkThis doctoral thesis outlines advances in multi-way analysis for characterizing
electroencephalogram (EEG) recordings from a paediatric population, with the aim to
describe new links between EEG data and changes in the brain. This entails establishing the
validity of multi-way analysis as a framework for identifying developmental information at the
individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear
algebraic architecture to identify latent structural relationships in naturally occurring higher
order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a
multi-way model to efficiently express the complex structures present in paediatric EEG
recordings as unique combinations of low-rank matrices, offering new insights into child
development. This multi-way CPD framework is explored for both typically developing (TD)
children and children with potential developmental delays (DD), e.g. children who suffer from
epilepsy or paediatric stroke.
Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric
EEG via multi-way analysis. Here, the CPD model probes the underlying relationships
between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the
CPD can reveal distinct population-level features in rEEG that reflect unique developmental
traits in varying child populations. These development-affiliated profiles are evaluated with
respect to capturing structures well-established in childhood EEG. The identified features are
also interrogated for their predictive abilities in anticipating new subjects’ ages. Assessing
simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis
framework as well suited for identifying developmental profiles from paediatric rEEG.
We extend the multi-way analysis scheme to more complex EEG scenarios common in
EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of
multi-way modelling for interventions where developmental changes often pose as barriers.
The multi-way CPD model is expanded to include four modes- task, spatial, spectral and
subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual
attention task that elucidates a steady-state visual evoked potential and present the advantages
gained from the extended CPD model. Through direct multi-linear projection, we demonstrate
that linear profiles of the CPD can be capitalized upon for rapid task classification sans
individual subject classifier calibration.
Incorporating concepts from the multi-way analysis scheme with child development measured
by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for
inferring development from paediatric EEG. We utilize a common EEG task (button-press) to
establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model
and cognitive scores from psychometric evaluations then permits joint decomposition of the
two datasets to identify common features associated with each representation of development.
Use of grid search optimization and a fully cross-validated design supports the JEDI model as
another technique for rapidly discerning the developmental status of a child via EEG.
We then briefly turn our attention to associating child development as measured by
psychometric tests to markers in the EEG using graph network properties. Using graph
networks, we show how the functional connectivity can inform on potential developmental
delays in very young epileptic children using routine, clinical rEEG measures. This establishes
a potential tool complementary to the JEDI model for identifying and inferring links between
the established psychometric evaluation of developing children and functional analysis of the
EEG.
Multi-way analysis of paediatric EEG data offers a new approach for handling the
developmental status and profiles of children. The CPD model offers flexibility in terms of
identifying development-related features, and can be integrated into EEG tasks common in
rehabilitation paradigms. We aim for the multi-way framework and associated techniques
pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for
characterizing paediatric development
Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals"
No full textMultiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEµ). We investigate the behaviour of these multivariate methods on multichannel white Gaussian and 1/f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEµ lead to more stable results and are less sensitive to the signals’ length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offer complexity profiles with complementary information for biomedical signal analysis. We made freely available all the Matlab codes used in this study, including mvSE, mvFE, mvMSEµ, RCmvMSEµ, mvMFEµ, RCmvMFEµ, mvMSEσ2, RCmvMSEσ2, mvMFEσ2 and RCmvMFEσ2.Azami, Hamed; Escudero, Javier. (2016). Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals", [software]. University of Edinburgh, School of Engineering, Institute for Digital Communications. http://dx.doi.org/10.7488/ds/143
Matlab codes for "Improved Multiscale Permutation Entropy for Biomedical Signal Analysis: Interpretation and Application to Electroencephalogram Recordings"
No full textPermutation entropy (PE) is a well-known and fast method extensively used in many physiological signal processing applications to measure the irregularity of time series. Multiscale PE (MPE) is based on assessing the PE for a number of coarse-grained sequences representing temporal scales. However, the stability of the conventional MPE may be compromised for short time series. Here, we propose an improved MPE (IMPE) to reduce the variability of entropy measures over long temporal scales, leading to more reliable and stable results. We gain insight into the dependency of MPE and IMPE on several straightforward signal processing concepts which appear in biomedical activity via a set of synthetic signals. We also apply these techniques to real biomedical signals via publicly available electroencephalogram (EEG) recordings acquired with eyes open and closed and to ictal and non-ictal intracranial EEGs. We conclude that IMPE improves the reliability of the entropy estimations in comparison with the traditional MPE and that it is a promising technique to characterize physiological changes affecting several temporal scales. We provide the codes of the synthetic signals and IMPE in the public domain.Hamed Azami, Javier Escudero, Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings, Biomedical Signal Processing and Control, Volume 23, January 2016, Pages 28-41, ISSN 1746-8094, http://dx.doi.org/10.1016/j.bspc.2015.08.004. (http://www.sciencedirect.com/science/article/pii/S174680941500138X
Design and application of dispersion entropy algorithms for physiological time-series analysis
Full text linkChanges in the variability of recorded physiological time-series have been connected with transitions in the state of the monitored physiological system. The two primary paradigms describing this connection are the Critical Slow Down (CSD) and the Loss of Complexity (LoC) paradigms. The CSD paradigm considers that during frail or pathological states, a slowing down is observed in the capacity of the system to recover from external stressors resulting in increased output complexity for certain regulated variables. The LoC paradigm suggests that when the equilibrium of a system is disrupted, certain effector variables that displayed multi-scale complexity produce output measurements of reduced variability indicating a loss in the system’s flexibility and capacity to adapt in the presence of external stressors.
For this purpose, entropy has emerged as a prominent nonlinear metric capable of assessing the non-linear dynamics and variability of time-series. Consequently, multiple entropy quantification algorithms have been developed for the analysis of time-series. These algorithms are based on Shannon Entropy such as the Permutation Entropy and Dispersion Entropy (DisEn) algorithms; and on Conditional Entropy such as the Sample Entropy and Fuzzy Entropy algorithms.
Within the scope of this study, the univariate and multivariate DisEn algorithms, first introduced in 2016 and in 2019 respectively, are used as the foundation and benchmark for the introduction of novel algorithmic variations. The selection of the DisEn algorithms is made due to their capability of producing features with significant discrimination capacity taking into consideration amplitude-based information while maintaining a linear computational complexity and having a functional multivariate variation capable of quantifying cross-channel dynamics. To initially ensure the effective quantification of DisEn during univariate physiological timeseries analysis, the effect of missing and outlier samples, which are common occurrence in physiological recordings, is studied and quantified. To improve algorithmic robustness, novel variations of the univariate DisEn algorithm are introduced for the analysis of low recording quality time-series. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate variability, electroencephalogram, and respiratory impedance time-series. The analysis indicates that missing samples have a reduced effect on the output DisEn and the error percentage can be maintained at values lower than 8% with the introduction of a variation that skips invalid values. Contrary to missing samples, outliers have a major disruptive effect with error percentages in the range of 57% to 73% for the original DisEn algorithm that is limited in values lower than 22% with the introduction of respective variations.
To expand the study from univariate to multivariate analysis, the multivariate DisEn algorithm is applied to physiological network segments formulated from multi-channel recordings of synchronized electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The effect of outliers, present across different channels, is quantified for both univariate and multivariate DisEn features. The sensitivity of DisEn features to outliers is utilized for the detection of artifactual network segments using logistic regression classifiers. Two variations of the classifier are deployed in several experimental setups, with the first utilizing solely univariate and the second both univariate and multivariate DisEn features. Noteworthy performance is achieved, with the percentage of correct network segment classifications surpassing 95% in a number of experimental setups, for both configurations.
Finally, to improve DisEn quantification during the analysis of multivariate systems for physiological monitoring applications, the framework of Stratified Entropy is introduced. Based on the framework, a set of strata with a clear hierarchy of prioritization are defined. Each channel of an input multi-channel time-series is allocated to a stratum and their contribution to the output DisEn value is determined by their allocation. Three novel Stratified DisEn algorithms are presented, as implementations of the framework, allowing multivariate analysis with controllable contribution from each channel to the output DisEn value. The original algorithm and the novel variations are implemented on synthetic time-series consisting of 1/f and white Gaussian noise, waveform physiological time-series and derived physiological data. The introduced Stratified DisEn variations operate as expected and correctly prioritize the channels allocated to the primary stratum of the hierarchy across all synthetic time-series setups. The results of waveform physiological time-series indicate that certain of the novel features extracted through Stratified DisEn achieve effect size increases in the range of 0.2 to 1.4 when separating between states of healthy sleep and sleep with obstructive sleep apnea. The derived physiological data results further highlight the increased discrimination capacity of the novel features with increases in the range of 5% to 30% in the mean absolute difference between values extracted during steady versus stressful physiological states. Furthermore, an example of decrease in the output DisEn values when moving from a steady to a stressful physiological state is highlighted during the prioritization of the heart rate channel, in alignment with LoC, providing an example of how Stratified Entropy could be used to test hypothesis based on the CSD and LoC paradigms.
By making steps towards addressing the challenge of low data quality and providing a new framework of analysis, this thesis aims to improve the process of assessing and measuring the variability of physiological time-series, leading to the consequent extraction of viable physiological information
Detection of developmental deficits in epileptic children using multimodal tensor decomposition techniques
No full textEarly childhood epilepsy can affect the child’s development and lead to developmental deficits.
Early detection and intervention are key to enabling the child to develop normally. Resting
state electroencephalogram (EEG) and Magnetic resonance imaging (MRI) are the main tools
clinicians use to diagnose children with epilepsy. This motivates us to take advantage of
these available data and jointly analyse them to explore the features related to developmental
deficits and predict the developmental scores of newly-onset patients. In particular, our work
considers EEG information, sMRI volumetric data, and psychometric evaluation scores. We
use matrix-tensor decompositions to analyse the shared features between each modality all
at once. This allows us to investigate the occurrence of shared profiles in EEG and sMRI
related to developmental impairment. Hence, this thesis develops data fusion methods based
on well-established tensor decomposition methods (canonical polyadic decomposition, CPD;
block term decomposition, BTD; and Tucker decomposition, TD). The methods are validated
in a publicly available dataset with healthy children (Child Mind Institute: CMI) and, more
importantly, in a local dataset of preschool children with epilepsy (NEUROPROFILE: Neu).
First, the thesis focuses on a CPD data fusion model, which decomposes the multi-way data
into a sum of rank-one factor matrices with the subject factor shared across three modalities.
The model is optimised via grid search. The CPD model reveals distinct features associated
with developmental deficits that agree with prior clinical knowledge. Then, we expand the
model through direct projection to predict the developmental scores from EEG and sMRI
data. A support vector machine (SVM) is used as a benchmark to compare the predicted score
performance. The result reveals CPD model is better at estimating the developmental scores
than the SVM. The CPD shows the feasibility of score prediction but still lacks the ability to
correctly identify the deficits, which highlights the need for a more flexible data fusion model.
Next, the thesis adopts block term decomposition (BTD) to bring in additional flexibility in the
modelling of the EEG tensor data. In BTD (Lᵣ,Lᵣ, 1), one mode of interest is fixed to rank
one while the others vary together to rank L. Subjects with missing scores and more sMRI
regions and sub-scores are included in this analysis. Bayesian optimisation is applied to reduce
the hyperparameter optimisation time. The results show that BTD (Lᵣ,Lᵣ, 1) can extract
additional features related to the deficits that the CPD model does not pick up. Then, we built
a model to predict the developmental scores. Overall, the prediction from BTD is generally
better than the CPD. However, the result shows both models may not be fully compatible with
EEG tensors and suggests the need for a better-fit model.
Therefore, we adopt TD as a flexible model for the EEG data. TD can decompose tensors into
factor matrices with different ranks interacting through a core tensor. However, TD without
constraints is not unique. Thus, we promote the sparseness in the TD core tensor in our
joint decomposition. In addition, we use structural connectivity information in the form of
diffusion tensor imaging (DTI) as a graph regularisation to the data fusion model to promote
interpretability. The effects of each constraint are investigated, and the most stable result is
extended to predict the scores. Since not all the patients have DTI data, the score prediction is
executed for both patients with and without DTI. Implementing the DTI graph regularisation
is found to result in predicted scores in a more plausible range. The sparse core TD with graph
regularisation performs best with the Neu dataset. However, some deficit patients are estimated
to score within the normal range, which does not fulfil the aim of identifying deficits accurately.
In addition, and given that the BTD (Lᵣ,Lᵣ, 1) tensor decomposition is closely related to
CPD, we investigate and expand the existing principle of CPD core consistency diagnosis
(CORCONDIA) to BTD (Lᵣ,Lᵣ, 1). BTDCORCONDIA is built to assist in determining
the number of components and the data compatibility to the model. The model is tested
with simulated and real EEG tensor data. We show that data generated with a unique core
compatible with BTD (Lᵣ,Lᵣ, 1) results in BTDCORCONDIA values of ∼ 100%. In contrast,
incompatible data will lead to low values. The result confirms that it is possible to perform a
core consistency diagnosis to check the compatibility between the model and data in BTD.
In summary, multimodal data fusion of paediatric brain data through matrix-tensor
decomposition offers a new approach to studying the shared underlying profiles and
developmental status of children with neurological diseases such as epilepsy. This could be
a stepping stone for future research seeking to integrate and adopt data fusion approaches
as additional tools for clinicians to prioritise children for an exhaustive assessment of their development
SUPERSEDED - Matlab codes for "Amplitude-aware Permutation Entropy: Illustration in Spike Detection and Signal Segmentation"
No full text## This item has been replaced by the one which can be found at https://datashare.ed.ac.uk/handle/10283/3864 ##
## Background and Objective: ##
Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values are considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE).
## Methods: ##
AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post-hoc Tukey’s test.
## Results: ##
In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE.
## Conclusion: ## We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE.Permutation entropy (PE) has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values are considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we proposed a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). For more information, please see reference [1].
* AAPE1: address the first above mentioned problem
* AAPE2: address the second above mentioned problem
* AAPE: address both of the above mentioned problems
* Ref: [1] H. Azami and J. Escudero, Amplitude-aware Permutation Entropy: Illustration in Spike Detection and Signal Segmentation Computer Methods and Programs in Biomedicine, 2016.
If you use the code, please make sure that you cite reference [1].
Hamed Azami and Javier Escudero Rodriguez
[email protected] and [email protected]
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