153 research outputs found
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
SUPERSEDED - Matlab codes for "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis"
No full text## This item has been superseded. It contained code for Azami, H., Fernández, A. & Escudero, J. Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Med Biol Eng Comput 55, 2037–2052 (2017). For up-to-date versions of this and other entropy analysis algorithms, consider visiting the GitHub repositories linked in the full item record. ##
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of fluctuations in the local mean value of biomedical time series. Recent developments in the field have tried to improve the MSE by reducing its variability in large scale factors. On the other hand, there has been recent interest in using other statistical moments than the mean, i.e. variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) to quantify the dynamical properties of spread over multiple time scales. We demonstrate the dependency of the RCMFEσ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. We also investigate the complementarity of using the standard deviation instead of the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicate that RCMFEσ offers complementary information to that revealed by classical coarse-graining approaches and that it has superior performance to distinguish different types of physiological activity.
The codes for our analysis, including sample entropy, fuzzy entropy, MSE based on mean (MSEμ), MFEμ, RCMSEμ, RCMFEμ, MSE based on variance (MSEσ2) , MFEσ2 , RCMSEσ2 , RCMFEσ2, MSEσ, MFEσ, RCMSEσ, and RCMFEσ are available here
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]
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.mvSE: multivariate sample entropy
mvFE: multivariate fuzzy entropy
mvMSE_mu: multivariate multiscale sample entropy whose coarse-graining is based on mean
RCmvMSE_mu: refined composite multivariate multiscale sample entropy whose coarse-graining is based on mean
mvMFE_mu: multivariate multiscale fuzzy entropy whose coarse-graining is based on mean
RCmvMFE_mu: refined composite multivariate multiscale fuzzy entropy whose coarse-graining is based on mean
mvMSE_var: multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
RCmvMSE_var: refined composite multivariate multiscale sample entropy whose coarse-graining is based on variance
mvMFE_var: multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
RCmvMFE_var: refined composite multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
embd: multivariate delay embedded vector
SUPERSEDED - Matlab codes for "Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals"
No full textPlease see the updated Matlab codes for "Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals" at https://doi.org/10.7488/ds/1982 . Multiscale entropy (MSE) is a widely-used tool for the analysis of biomedical signals. It was proposed to overcome the deficiencies of conventional entropy methods when quantifying the complexity of time series. However, MSE is undefined for very short signals and slow for real-time applications as a result of using sample entropy (SampEn). To overcome these shortcomings, 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 SampEn. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and find that these methods show similar results but the MDE and RCMDE are significantly faster than MSE and RCMSE, respectively. The results also show that RCMDE is more stable than MDE and RCMSE for short and noisy signals, which are common in biomedical applications. To evaluate the proposed methods on real signals, three biomedical datasets, including focal and non-focal electroencephalograms (EEGs), blood pressure recordings in Fantasia database, and resting-state EEGs activity in Alzheimer's disease, are used. The results again show similar trends of RCMSE, MDE, and RCMDE, although the RCMDE and MDE are significantly faster and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. To sum up, 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 here
Amplitude- and Fluctuation-Based Dispersion Entropy
Get PDFDispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326
Matlab codes for "Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals"
No full textMultiscale entropy (MSE) is a widely-used tool for the analysis of biomedical signals. It was proposed to overcome the deficiencies of conventional entropy methods when quantifying the complexity of time series. However, MSE is undefined for very short signals and slow for real-time applications as a result of using sample entropy (SampEn). To overcome these shortcomings, 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 SampEn. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and find that these methods show similar results but the MDE and RCMDE are significantly faster than MSE and RCMSE, respectively. The results also show that RCMDE is more stable than MDE and RCMSE for short and noisy signals, which are common in biomedical applications. To evaluate the proposed methods on real signals, three biomedical datasets, including focal and non-focal electroencephalograms (EEGs), blood pressure recordings in Fantasia database, and resting-state EEGs activity in Alzheimer's disease, are used. The results again show similar trends of RCMSE, MDE, and RCMDE, although the RCMDE and MDE are significantly faster and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. To sum up, 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 here
HERMENEUTIKA OTENTISITAS HADIS M. MUSTOFA AZAMI
No full textThis article discusses Azami criticism to Ignaz Gholziher and Joseph Schacht. The author tries to integrate the relations of hermeneutics as a solution to solve the issues of Hadith authenticity, and its interconnected to psychology, that Azami position when criticizing the Orientalists, Joseph Schacht, based on the flow of his thought of isnaad. Then in reviewing hermeneutics, in general there are three dominant element is the relationship between (author), Text (text) and readers (reader). So the results of this analysis, the author is orientalist, Joseph Schacht Ignaz Goldziher. The Text is orientalist books, thoughts, opinions or their theories, in this case Joseph Schacht. The reader is referred Azami. The discovery of the authors that position Azami criticism included are the internal and external criticism, namely external criticism Azami focus lies in criticism of the Orientalists, he criticized Joseph from isnaad. And internal criticism, plays on historiography, he criticized the use of sciences related to hadith, such as ‘Ilm Tadwin al-Hadith, ‘Ilm Rijal al-Hadith, ‘Ilm Jarh wa ta\u27dil, Ulum al-Hadith, ‘Ilm al-Fiqh.Dalam tulisan ini dikaji Kritik Azami terhadap kedua orientalis yaitu Ignaz Gholziher dan Joseph Schacht. Penulis mencoba mengintegrasikan bagaimana hubungan hermeneutika sebagai solusi untuk memecahkan isu-isu otentisitas Hadis, dan aplikasi interkoneksinya seperti ilmu psikologi, bahwa posisi Azami ketika mengkritik terhadap orientalis yaitu Joseph, ia melihat berdasarkan alur pemikiran isnadnya. Kemudian dalam mengkaji hermeneutika, secara garis besar ada tiga unsur yang dominan yaitu hubungan antara (author), teks (text) dan pembaca (reader). Maka hasil dari analisis ini, author adalah orientalis yaitu Joseph Schacht, Ignaz Goldziher yang mana textnya adalah buku-buku orientalis, pemikiran, pendapat atau teori orientalis dalam hal ini Joseph Schacht, yang dimaksud reader adalah Azami. Penemuan penulis bahwa posisi kritik Azami termasuk berada dalam kritik internal dan eksternal, yaitu fokus kritik eksternal Azami terletak pada kritik terhadap orientalis, ia mengkritik Joseph dari isnadnya. Dan kritik internal, berposisi pada historiografi, ia mengkritik menggunakan ilmu-ilmu yang berkaitan dengan hadis, seperti ilmu tadwin al-hadis, ilmu rijalul hadis, ilmu jar wata’dil, Ilmu Hadis, ilmu Fiqih
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
Kajian Hadis Mustafa Azami Sebagai Kerja Hermeneutika (Analisis Kajian Sanad dan Matan Hadis dalam Studies in Hadith Methodologi and Literature Karya Mustafa Azami)
Get PDFyang tak terelakkan dalam kajian hadis. Dari sini penting disadari bahwa hermeneutika bukan hal baru, apalagi “sesuatu” yang berbahaya bagi kajian Hadis. Istilah ini memang bukan dari pemikir Islam. Namun secara subtansi, hermeneutika sebagai kerja kritis atas hadis (sanad dan matan) telah melekat di kalangan muslim klasik dan modern-kontemporer. Tulisan ini ingin membuktikan bahwa Azami sekalipun, yang dikenal ‘anti’ barat, secara subtansi melakukan kerja hermeneutika. Metode kajian hadis Azami, baik sanad maupun matan akan ditarik dalam diskusi hermeneutika hadis, yang dalam hal ini penulis akan menggunakan tiga unsur dasar dalam wacana hermeneutika, yakni author (perawi), teks (hadis) dan reader (Azami). Artikel ini akan menjawan tentag bagaimana dan sejauhmana metode pemikiran hadis Mustafa Azami dapat diposisikan sebagai kerja hermeneutika, dalam hal ini sebagai kajian kritis atas sanad dan matan hadis? Hasil kajian menunjukkan bahwa Azami dalam kerja hermeneutika-nya senantiasa mengungkap diskusi keorisinalitas perawi (sanad) dan kerasionalitas matan dengan melakukan metode perbandingan. Argumen nalar digunakan dalam konteks menelusuri seputar fakta perawi, dan menentukan masuk akal atau tidaknya kandungan matan hadi
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