PhysioToolkit

PhysioToolkit is a large and growing library of software for physiologic signal processing and analysis, detection of physiologically significant events using both classical techniques and novel methods based on statistical physics and nonlinear dynamics, interactive display and characterization of signals, creation of new databases, simulation of physiologic and other signals, quantitative evaluation and comparison of analysis methods, and analysis of nonequilibrium and nonstationary processes. A unifying theme of the research projects that contribute software to PhysioToolkit is the extraction of “hidden” information from biomedical signals, information that may have diagnostic or prognostic value in medicine, or explanatory or predictive power in basic research. All PhysioToolkit software is available in source form under the GNU General Public License (GPL).


References in zbMATH (referenced in 62 articles )

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  1. Christopoulos, Stavros-Richard G.; Sarlis, Nicholas V.: An application of the coherent noise model for the prediction of aftershock magnitude time series (2017)
  2. Coelho, V.N.; Coelho, I.M.; Coelho, B.N.; Souza, M.J.F.; Guimarães, F.G.; Luz, E.J.da S.; Barbosa, A.C.; Coelho, M.N.; Netto, G.G.; Costa, R.C.; Pinto, A.A.; Figueiredo, A.de P.; Elias, M.E.V.; Filho, D.C.O.G.; Oliveira, T.A.: EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS (2017)
  3. Kizilkaya, Aydin; Elbi, Mehmet D.: Time-varying weighted optimal empirical mode decomposition using multiple sets of basis functions (2017)
  4. Liu, Manxia; Hommersom, Arjen; van der Heijden, Maarten; Lucas, Peter J.F.: Hybrid time Bayesian networks (2017)
  5. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  6. Ravan, Maryam: Beamspace fast fully adaptive brain source localization for limited data sequences (2017)
  7. Ródenas, Juan; García, Manuel; Alcaraz, Raúl; Rieta, José J.: Combined nonlinear analysis of atrial and ventricular series for automated screening of atrial fibrillation (2017)
  8. Tekumalla, Lavanya Sita; Rajan, Vaibhav; Bhattacharyya, Chiranjib: Vine copulas for mixed data: multi-view clustering for mixed data beyond meta-Gaussian dependencies (2017)
  9. Bandt, Christoph: Permutation entropy and order patterns in long time series (2016)
  10. Deepa, K.G.; Ambat, Sooraj K.; Hari, K.V.S.: Fusion of sparse reconstruction algorithms for multiple measurement vectors (2016)
  11. Krafty, Robert T.: Discriminant analysis of time series in the presence of within-group spectral variability (2016)
  12. Ma, Yurun; Li, Tongqing; Ma, Yide; Zhan, Kun: Novel real-time FPGA-based R-wave detection using lifting wavelet (2016)
  13. Rakshit, M.; Panigrahy, D.; Sahu, P.K.: An improved method for R-peak detection by using Shannon energy envelope (2016)
  14. Suk, Hye Won; Hwang, Heungsun: Functional generalized structured component analysis (2016)
  15. Van Esbroeck, Alex; Smith, Landon; Syed, Zeeshan; Singh, Satinder; Karam, Zahi: Multi-task seizure detection: addressing intra-patient variation in seizure morphologies (2016)
  16. Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E.: Scale-dependent intrinsic entropies of complex time series (2016)
  17. Arias-Londoño, Julián D.; Godino-Llorente, Juan I.: Entropies from Markov models as complexity measures of embedded attractors (2015)
  18. Fresnel, Emeline; Yacoub, Emad; Freitas, Ubiratan; Kerfourn, Adrien; Messager, Valérie; Mallet, Eric; Muir, Jean-François; Letellier, Christophe: An easy-to-use technique to characterize cardiodynamics from first-return maps on $\Delta$RR-intervals (2015)
  19. Hooker, Giles; Ellner, Stephen P.: Goodness of fit in nonlinear dynamics: misspecified rates or misspecified states? (2015)
  20. Huang, Xiao-Lin; Shi, Lei; Yan, Ming: Nonconvex sorted $\ell_1$ minimization for sparse approximation (2015)

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