| 1. |
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| 2. |
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| 3. |
- Bentham, James, et al.
(författare)
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A century of trends in adult human height
- 2016
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Ingår i: eLIFE. - : eLife Sciences Publications Ltd. - 2050-084X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Being taller is associated with enhanced longevity, and higher education and earnings. We reanalysed 1472 population-based studies, with measurement of height on more than 18.6 million participants to estimate mean height for people born between 1896 and 1996 in 200 countries. The largest gain in adult height over the past century has occurred in South Korean women and Iranian men, who became 20.2 cm (95% credible interval 17.5–22.7) and 16.5 cm (13.3– 19.7) taller, respectively. In contrast, there was little change in adult height in some sub-Saharan African countries and in South Asia over the century of analysis. The tallest people over these 100 years are men born in the Netherlands in the last quarter of 20th century, whose average heights surpassed 182.5 cm, and the shortest were women born in Guatemala in 1896 (140.3 cm; 135.8– 144.8). The height differential between the tallest and shortest populations was 19-20 cm a century ago, and has remained the same for women and increased for men a century later despite substantial changes in the ranking of countries.
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| 4. |
- Bayisa, Fekadu, et al.
(författare)
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Adaptive algorithm for sparse signal recovery
- 2019
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Ingår i: Digital signal processing (Print). - : Elsevier. - 1051-2004 .- 1095-4333. ; 87, s. 10-18
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Tidskriftsartikel (refereegranskat)abstract
- The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing “adaptive sparsity matching pursuit” and “alternating direction method of multipliers” methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms.
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| 5. |
- Bayisa, Fekadu, et al.
(författare)
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Model-based computed tomography image estimation : partitioning approach
- 2019
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Ingår i: Journal of Applied Statistics. - : Taylor & Francis. - 0266-4763 .- 1360-0532. ; 46:14, s. 2627-2648
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. The existing model-based methods perform poorly on bone tissues. This paper was aimed at obtaining improved bone tissue estimation. Skew-Gaussian mixture model and Gaussian mixture model were proposed to investigate CT image estimation from MR images by partitioning the data into two major tissue types. The performance of the proposed models was evaluated using the leaveone-out cross-validation method on real data. In comparison with the existing model-based approaches, the model-based partitioning approach outperformed in bone tissue estimation, especially in dense bone tissue estimation.
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| 6. |
- Bayisa, Fekadu, et al.
(författare)
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Model-based Estimation of Computed Tomography Images
- 2017
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Ingår i: 3rd International Researchers, Statisticians and Young Statisticians Congress. - : Selcuk University. ; , s. 84-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Statistical methods are required to estimate computed tomography (CT) images from magnetic resonance (MR) images. The main purpose of estimating CT images was to get a fully MR based radiotherapy. Specifically, bone tissues and air are indistinguishable on MR images. But, there is a good contrast between soft tissue and other tissues on MR images. On CT images, there is eyecatching contrast between bone and non-bone tissues. Therefore, the main reason for CT estimation is to get improved bone tissues estimation and to use the estimated CT in fully MR based radiotherapy. The estimated CT images (also called substitute CT or Pseudo-CT images) are used for attenuation correction and dose planning in MR based radiotherapy. Gaussian mixture model (GMM) is used to investigate CT image estimation from MR images without taking spatial information into account. Markov random field (MRF) and hidden Markov model (HMM) are used to extend the approach by taking spatial dependence into account. Leave-one-dataset-out cross-validation method on five datasets (obtained from head of five patients) is used to evaluate the performance of the models. In terms of MAE, the use of spatial information improves the overall quality of CT image estimation. In this application, HMM is computationally faster and has superior performance on MRF. However, it has poor performance on bone tissues. On the other hand, MRF is computationally expensive and intractable for log-likelihood based model diagnostic. These two behaviour of HMM and MRF motivated this work to further probe the estimation of CT images from MR images by partitioning the data into bone and non-bone tissues. The partitioning of the data was based on CT value threshold. Skew-Gaussian mixture model (SGMM) and GMM applied on each partition. In terms of MAE, SGMM and GMM* (GMM applied to each partition) performed better than HMM and MRF on the bone tissues.
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| 7. |
- Bayisa, Fekadu, et al.
(författare)
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Model-based Estimation of Computed Tomography Images
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. Existing model-based methods have performed poorly on bone tissues. This paper aims to obtainimproved estimation of bone tissues. Skew-Gaussian mixture model (SGMM) isproposed to further investigate CT image estimation from MR images. The estimation quality of the proposed model is evaluated using leave-one-out cross-validation method on real data. In comparison with the existing model-based approaches, the approach utilized in this paper outperforms in estimation of bone tissues, especiallyon dense bone tissues.
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| 8. |
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| 9. |
- Bayisa, Fekadu, et al.
(författare)
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Statistical learning in computed tomography image estimation
- 2018
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Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 45:12, s. 5450-5460
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications
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| 10. |
- Brynolfsson, Patrik, et al.
(författare)
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Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling
- 2015
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Ingår i: Magnetic Resonance in Medicine. - : Wiley-Blackwell. - 0740-3194 .- 1522-2594. ; 74:4, s. 1156-1164
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.
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| 11. |
- Cronie, Ottmar, et al.
(författare)
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Functional marked point processes : Unifying spatio-temporal frameworks and analysing spatially dependent functional data
- 2019
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Ingår i: Statistical Analysis for Space-Time Data. - : Eurpean Courses in Advanced Statistics (ECAS). ; , s. 7-7
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Konferensbidrag (refereegranskat)abstract
- This paper treats functional marked point processes (FMPPs), which are defined as marked point processes where the marks are random elements in some (Polish) function space. Such marks may represent e.g. spatial paths or functions of time. To be able to consider e.g. multivariate FMPPs, we also attach an additionally, Euclidean, mark to each point. We indicate how the FMPP framework quite naturally connects the point process framework with both the functional data analysis framework and the geostatistical framework; in particular we define spatio-temporal geostatistical marking for point processes. We further show that various existing stochastic models fit well into the FMPP framework, in particular marked point processes with real valued marks. To be able to carry out non-parametric statistical analyses for functional marked point patterns, we study characteristics such as product densities and Palm distributions, which are the building blocks for summary statistics such as marked inhomogeneous J-functions and our so-called K-functionals. We finally apply these statistical tools to analyse a few different functional marked point patterns.
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| 12. |
- Cronie, Ottmar, et al.
(författare)
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Maximum likelihood estimation in a discretely observed immigration-death process
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In order to find the maximum likelihood (ML) estimator of the parameter pair governing the immigration-death process (a continuous time Markov chain) we derive its transition probabilities. The likelihood maximisation problem is reduced from two dimensions to one dimension. We also show the consistency and the asymptotic normality of the ML-estimator under an equidistant sampling scheme, given that the parameter pair lies in some compact subset of the positive part of the real plane. We thereafter evaluate, numerically, the behaviour of the estimator and we finally see how our ML-estimation can be applied to the so-called Renshaw-Särkkä growth interaction model; a spatio-temporal point process with time dependent interacting marks in which the immigration-death process controls the arrivals of new marked points as well as their potential life-times.
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| 13. |
- Cronie, Ottmar, et al.
(författare)
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Spatio-Temporal Modelling of Swedish Scots Pine Stands
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Considering measurements of locations and radii at breast height made at three different time points of the individual trees in ten Swedish Scots pine plots, we employ the so called growth-interaction (GI) process for the spatio-temporal modelling of the plots. The GI-process places trees at random locations in the study region and assigns radii (sizes) to the trees, which interact and grow with time. It has been used to model Scots pine plots in previous studies, and to improve the fit we suggest some modifications of the model: A different location assignment strategy and a different function for the open-growth (growth in absence of competition). We believe also that the space-time data contain too small trees to reflect the open-growth properly, which primarily affectsthe carrying capacity parameter. We evaluate the open-growth froma separate set of data which consists of size and age measurements ofolder and larger single Scots pines. This data set better represents the open-growth of Scots pines than the space-time data sets. A linear relationship is found between the estimated site indexes of the plots and the sizes, and this relationship is exploited in the estimation of the carrying capacity. For each of the ten space-time data sets (plots) we estimate the remaining parameters of the GI-process and finally, by means of some Monte Carlo tests, we test the goodness-of-fit of simulated predictions from the fitted model.
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| 14. |
- Cronie, Ottmar, et al.
(författare)
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The discretely observed immigration-death process : Likelihood inference and spatiotemporal applications
- 2016
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Ingår i: Communications in Statistics - Theory and Methods. - : Taylor & Francis Group. - 0361-0926 .- 1532-415X. ; 45:18, s. 5279-5298
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Tidskriftsartikel (refereegranskat)abstract
- We consider a stochastic process, the homogeneous spatial immigration-death (HSID) process, which is a spatial birth-death process with as building blocks (i) an immigration-death (ID) process (a continuous-time Markov chain) and (ii) a probability distribution assigning iid spatial locations to all events. For the ID process, we derive the likelihood function, reduce the likelihood estimation problem to one dimension, and prove consistency and asymptotic normality for the maximum likelihood estimators (MLEs) under a discrete sampling scheme. We additionally prove consistency for the MLEs of HSID processes. In connection to the growth-interaction process, which has a HSID process as basis, we also fit HSID processes to Scots pine data.
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| 15. |
- Dadras, Ali, et al.
(författare)
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A ridgelet approach to poisson denoising
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper introduces a novel ridgelet transform-based method for Poisson image denoising. Our work focuses on harnessing the Poisson noise's unique non-additive and signal-dependent properties, distinguishing it from Gaussian noise. The core of our approach is a new thresholding scheme informed by theoretical insights into the ridgelet coefficients of Poisson-distributed images and adaptive thresholding guided by Stein's method. We verify our theoretical model through numerical experiments and demonstrate the potential of ridgelet thresholding across assorted scenarios. Our findings represent a significant step in enhancing the understanding of Poisson noise and offer an effective denoising method for images corrupted with it.
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| 16. |
- Fries, Niklas, 1991-
(författare)
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Data-driven quality management using explainable machine learning and adaptive control limits
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In industrial applications, the objective of statistical quality management is to achieve quality guarantees through the efficient and effective application of statistical methods. Historically, quality management has been characterized by a systematic monitoring of critical quality characteristics, accompanied by manual and experience-based root cause analysis in case of an observed decline in quality. Machine learning researchers have suggested that recent improvements in digitization, including sensor technology, computational power, and algorithmic developments, should enable more systematic approaches to root cause analysis.In this thesis, we explore the potential of data-driven approaches to quality management. This exploration is performed with consideration to an envisioned end product which consists of an automated data collection and curation system, a predictive and explanatory model trained on historical process and quality data, and an automated alarm system that predicts a decline in quality and suggests worthwhile interventions. The research questions investigated in this thesis relate to which statistical methods are relevant for the implementation of the product, how their reliability can be assessed, and whether there are knowledge gaps that prevent this implementation.This thesis consists of four papers: In Paper I, we simulated various types of process-like data in order to investigate how several dataset properties affect the choice of methods for quality prediction. These properties include the number of predictors, their distribution and correlation structure, and their relationships with the response. In Paper II, we reused the simulation method from Paper I to simulate multiple types of datasets, and used them to compare local explanation methods by evaluating them against a ground truth.In Paper III, we outlined a framework for an automated process adjustment system based on a predictive and explanatory model trained on historical data. Next, given a relative cost between reduced quality and process adjustments, we described a method for searching for a worthwhile adjustment policy. Several simulation experiments were performed to demonstrate how to evaluate such a policy.In Paper IV, we described three ways to evaluate local explanation methods on real-world data, where no ground truth is available for comparison. Additionally, we described four methods for decorrelation and dimension reduction, and describe the respective tradeoffs. These methods were evaluated on real-world process and quality data from the paint shop of the Volvo Trucks cab factory in Umeå, Sweden.During the work on this thesis, two significant knowledge gaps were identified: The first gap is a lack of best practices for data collection and quality control, preprocessing, and model selection. The other gap is that although there are many promising leads for how to explain the predictions of machine learning models, there is still an absence of generally accepted definitions for what constitutes an explanation, and a lack of methods for evaluating the reliability of such explanations.
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| 17. |
- Garpebring, Anders, et al.
(författare)
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Uncertainty estimation in dynamic contrast-enhanced MRI
- 2013
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Ingår i: Magnetic Resonance in Medicine. - : Wiley-Blackwell. - 0740-3194 .- 1522-2594. ; 69:4, s. 992-1002
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Tidskriftsartikel (refereegranskat)abstract
- Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.
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| 18. |
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| 19. |
- Ghorbani, Mohammad, et al.
(författare)
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Functional marked point processes : a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
- 2021
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Ingår i: Test (Madrid). - : Sociedad de Estadística e Investigación Operativa (Spanish Society of Statistics and Operations Research). - 1133-0686 .- 1863-8260. ; 30:3, s. 529-568
-
Tidskriftsartikel (refereegranskat)abstract
- This paper treats functional marked point processes (FMPPs), which are defined as marked point processes where the marks are random elements in some (Polish) function space. Such marks may represent, for example, spatial paths or functions of time. To be able to consider, for example, multivariate FMPPs, we also attach an additional, Euclidean, mark to each point. We indicate how the FMPP framework quite naturally connects the point process framework with both the functional data analysis framework and the geostatistical framework. We further show that various existing stochastic models fit well into the FMPP framework. To be able to carry out nonparametric statistical analyses for FMPPs, we study characteristics such as product densities and Palm distributions, which are the building blocks for many summary statistics. We proceed to defining a new family of summary statistics, so-called weighted marked reduced moment measures, together with their nonparametric estimators, in order to study features of the functional marks. We further show how other summary statistics may be obtained as special cases of these summary statistics. We finally apply these tools to analyse population structures, such as demographic evolution and sex ratio over time, in Spanish provinces.
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| 20. |
- Hildeman, Anders, et al.
(författare)
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Hildeman, A., Bolin, D., Wallin, J., Johansson, A., Nyholm, T., Asklund, T., and Yu, J. Whole-brain substitute CT generation using Markov random field mixture models.
- 2016
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixture models can be used to generate a substitute CT (s-CT) image from a specific set of MRI modalities. This work introduces a more flexible class of mixture models for s-CT generation, that incorporates spatial dependency in the data through a Markov random field prior on the latent field of class memberships associated with a mixture model. Furthermore, the mixture distributions are extended from Gaussian to normal inverse Gaussian (NIG), allowing heavier tails and skewness. The amount of data needed to train a model for s-CT generation is of the order of 10^8 voxels. The computational efficiency of the parameter estimationand prediction methods are hence paramount, especially when spatial dependency is included in the models. A stochastic Expectation Maximization (EM) gradient algorithm is proposed in order to tackle this challenge. The advantages of the spatial model and NIG distributions are evaluated with a cross-validation study based ondata from 14 patients. The study show that the proposed model enhances the predictive quality of the s-CT images by reducing the mean absolute error with 17.9%. Also, the distribution of CT values conditioned on the MR images are better explainedby the proposed model as evaluated using continuous ranked probability scores.
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| 21. |
- Jiao, Xiang, et al.
(författare)
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Comparative analysis of nonlinear growth curve models for Arabidopsis thaliana rosette leaves
- 2018
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Ingår i: Acta Physiologiae Plantarum. - : Springer. - 0137-5881 .- 1861-1664. ; 40:6
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Tidskriftsartikel (refereegranskat)abstract
- As a model organism, modeling and analysis of the phenotype of Arabidopsis thaliana (A. thaliana) leaves for a given genotype can help us better understand leaf growth regulation. A. thaliana leaves growth trajectories are to be nonlinear and the leaves contribute most to the above-ground biomass. Therefore, analysis of their change regulation and development of nonlinear growth models can better understand the phenotypic characteristics of leaves (e.g., leaf size) at different growth stages. In this study, every individual leaf size of A. thaliana rosette leaves was measured during their whole life cycle using non-destructive imaging measurement. And three growth models (Gompertz model, logistic model and Von Bertalanffy model) were analyzed to quantify the rosette leaves growth process of A. thaliana. Both graphical (plots of standardized residuals) and numerical measures (AIC, R2 and RMSE) were used to evaluate the fitted models. The results showed that the logistic model fitted better in describing the growth of A. thaliana leaves compared to Gompertz model and Von Bertalanffy model, as it gave higher R2 and lower AIC and RMSE for the leaves of A. thaliana at different growth stages (i.e., early leaf, mid-term leaf and late leaf).
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| 22. |
- Johansson, Adam, et al.
(författare)
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Voxel-wise uncertainty in CT substitute derived from MRI
- 2012
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Ingår i: Medical physics (Lancaster). - : American Association of Physicists in Medicine. - 0094-2405. ; 39:6, s. 3283-3290
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences.Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities.Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation.Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.
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| 23. |
- Jurca, Manuela, et al.
(författare)
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Biotechnological adaptation of seasonal growth using high yielding Populus gibberellin overproducing trees
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Tree growth is central to terrestrial ecology and the forestry industry. The overproduction by biotechnological means of hormones such as gibberellins (GAs) has been used as a powerful toolto greatly increase tree yield and wood properties. However, for trees in temperate and boreal regions, overexpressing GAs prevents the ability to induce vegetative dormancy, and results in reduced yield and tree loss over time. Since Populus trees are using an internal 24-h (circadian) clock to synchronize their metabolism and growth with local, predictable changes in the external environment, we focused on circadian control of GA metabolism, to showcase the principle of seasonal growth adaptation. To obtain both maintained growth benefits and a seasonally timed growth, we set out to modulate levels of bioactive GAs by using the endogenous Populus tremula× P. tremuloides CycD3 promoter. We show that both high yield and biotechnical seasonal growth adaptation is obtained with this promoter, which is coordinated by the clock protein LATE ELONGATED HYPOCOTYL 2 (PttLHY2). This work paves the way for future precision breeding of trees with local adaptation and increased yield.
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| 24. |
- Karlsson, Stefan, et al.
(författare)
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Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods
- 1999
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Ingår i: IEEE Transactions on Biomedical Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9294 .- 1558-2531. ; 46:6, s. 670-684
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric(ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean squareerror (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolutionof frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates bysignificantly reducing the MSE’s for both Fourier and wavelet packet methods.
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| 25. |
- Karlsson, Stefan, et al.
(författare)
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Estimation of surface electromyogram spectral alteration using reduced-order autoregressive model
- 2000
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Ingår i: Medical and Biological Engineering and Computing. - 0140-0118 .- 1741-0444. ; 38, s. 520-527
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Tidskriftsartikel (refereegranskat)abstract
- A new method is proposed, based on the pole phase angle (PPA) of a second-order autoregressive (AR) model, to track spectral alteration during localised muscle fatigue when analysing surface myo-electric (ME) signals. Both stationary and non-stationary, simulated and real ME signals are used to investigate different methods to track spectral changes. The real ME signals are obtained from three muscles (the right vastus lateralis, rectus femoris and vastus medialis) of six healthy male volunteers, and the simulated signals are generated by passing Gaussian white-noise sequences through digital filters with spectral properties that mimic the real ME signals. The PPA method is compared, not only with spectra-based methods, such as Fourier and AR, but also with zero crossings (ZCs) and the first AR coefficient that have been proposed in the literature as computer efficient methods. By comparing the deviation (dev), in percent, between the linear regression of the theoretical and estimated mean frequencies of the power spectra for simulated stationary (s) and non-stationary (ns) signals, in general, it is found that the PPA method (dev(s) = 4.29; dev(ns) = 1.94) gives a superior performance to ZCs (dv(s) = 8.25) and the first AR coefficient (4.18
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