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Depositordc.contributorAzami, H
Funderdc.contributor.otherUniversity of Edinburghen_UK
Data Creatordc.creatorAzami, Hamed
Data Creatordc.creatorEscudero, Javier
Date Accessioneddc.date.accessioned2016-02-10T15:59:45Z
Date Availabledc.date.available2016-03-20T05:15:23Z
Citationdc.identifier.citationAzami, Hamed; Escudero, Javier. (2016). Matlab codes for "Amplitude-aware Permutation Entropy: Illustration in Spike Detection and Signal Segmentation", [software]. University of Edinburgh. School of Engineering, Institute for Digital Communications. https://doi.org/10.7488/ds/1339.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/1918
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/1339
Dataset Description (abstract)dc.description.abstract## 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.en_UK
Dataset Description (TOC)dc.description.tableofcontentsPermutation 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 hamed.azami@ed.ac.uk and javier.escudero@ed.ac.uken_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Engineering, Institute for Digital Communicationsen_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Subjectdc.subjectsignal irregularityen_UK
Subjectdc.subjectamplitude-aware permutation entropyen_UK
Subjectdc.subjectspike detectionen_UK
Subjectdc.subjectsignal segmentationen_UK
Subjectdc.subjectelectroencephalogramen_UK
Subjectdc.subjectextracellular neuronal dataen_UK
Subject Classificationdc.subject.classificationEngineering::Bioengineeringen_UK
Titledc.titleMatlab codes for "Amplitude-aware Permutation Entropy: Illustration in Spike Detection and Signal Segmentation"en_UK
Typedc.typesoftwareen_UK

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