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 128 articles )

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  1. Adrian Lamela, Itziar Fernandez, Yolanda Larriba, Alejandro Rodriguez, Cristina Rueda: FMM: An R Package for Modeling Rhythmic Patterns in Oscillatory Systems (2021) arXiv
  2. Erkuş, Ekin Can; Purutçuoğlu, Vilda: Outlier detection and quasi-periodicity optimization algorithm: frequency domain based outlier detection (FOD) (2021)
  3. Gnutti, Alessandro; Guerrini, Fabrizio; Adami, Nicola; Migliorati, Pierangelo; Leonardi, Riccardo: A wavelet filter comparison on multiple datasets for signal compression and denoising (2021)
  4. Gu, Danlei; Mi, Yujia; Lin, Aijing: Application of time-delay multiscale symbolic phase compensated transfer entropy in analyzing cyclic alternating pattern (CAP) in sleep-related pathological data (2021)
  5. Huang, Min; Sun, Zhongkui; Donner, Reik V.; Zhang, Jie; Guan, Shuguang; Zou, Yong: Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks (2021)
  6. Moghadam, Farangis Sajadi; Moridani, Mohammad Karimi; Jalilehvand, Yasaman: Analysis of heart rate dynamics based on nonlinear lagged returned map for sudden cardiac death prediction in cardiovascular patients (2021)
  7. Shang, Du; Shang, Pengjian; Zhang, Zuoquan: Efficient synchronization estimation for complex time series using refined cross-sample entropy measure (2021)
  8. Yao, Wenpo; Wang, Jun; Perc, Matjaž; Yao, Wenli; Dai, Jiafei; Guo, Daqing; Yao, Dezhong: Time irreversibility and amplitude irreversibility measures for nonequilibrium processes (2021)
  9. Ahn, Kyungmin; Tucker, J. Derek; Wu, Wei; Srivastava, Anuj: Regression models using shapes of functions as predictors (2020)
  10. Bandt, Christoph: Order patterns, their variation and change points in financial time series and Brownian motion (2020)
  11. Bhardwaj, Swati; Gudur, Venkateshwarlu Yellaswamy; Acharyya, Amit: An accelerated computational approach in proteomics (2020)
  12. Borin, Airton Monte Serrat jun.; Silva, Luiz Eduardo Virgilio; Murta, Luiz Otavio jun.: Modified multiscale fuzzy entropy: a robust method for short-term physiologic signals (2020)
  13. Giordano, Francesco; Coretto, Pietro: A Monte Carlo subsampling method for estimating the distribution of signal-to-noise ratio statistics in nonparametric time series regression models (2020)
  14. Matuk, James; Mohammed, Shariq; Kurtek, Sebastian; Bharath, Karthik: Biomedical applications of geometric functional data analysis (2020)
  15. Padhy, Sibasankar; Goovaerts, Griet; Boussé, Martijn; De Lathauwer, Lieven; van Huffel, Sabine: The power of tensor-based approaches in cardiac applications (2020)
  16. Schweinberger, Michael; Krivitsky, Pavel N.; Butts, Carter T.; Stewart, Jonathan R.: Exponential-family models of random graphs: inference in finite, super and infinite population scenarios (2020)
  17. Semak, Matthew R.; Schwartz, Jeremiah; Heise, Gary: Examining human unipedal quiet stance: characterizing control through jerk (2020)
  18. van der Walt, Maria D.: Empirical mode decomposition with shape-preserving spline interpolation (2020)
  19. Ciarrocchi, Nicolás; Quiróz, Nicolás; Traversaro, Francisco; Roman, Eduardo San; Risk, Marcelo; Goldemberg, Fernando; Redelico, Francisco O.: The complexity of intracranial pressure as an indicator of cerebral autoregulation (2019)
  20. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)

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