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 115 articles )

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  1. Bhardwaj, Swati; Gudur, Venkateshwarlu Yellaswamy; Acharyya, Amit: An accelerated computational approach in proteomics (2020)
  2. Bidias à. Mougoufan, Jean Bertin; Eyebe Fouda, J. S. Armand; Tchuente, Maurice; Koepf, Wolfram: Adaptive ECG beat classification by ordinal pattern based entropies (2020)
  3. Matuk, James; Mohammed, Shariq; Kurtek, Sebastian; Bharath, Karthik: Biomedical applications of geometric functional data analysis (2020)
  4. Mohanty, Monalisa; Biswal, Pradyut; Sabut, Sukanta: Machine learning approach to recognize ventricular arrhythmias using VMD based features (2020)
  5. Padhy, Sibasankar; Goovaerts, Griet; Boussé, Martijn; De Lathauwer, Lieven; van Huffel, Sabine: The power of tensor-based approaches in cardiac applications (2020)
  6. Semak, Matthew R.; Schwartz, Jeremiah; Heise, Gary: Examining human unipedal quiet stance: characterizing control through jerk (2020)
  7. 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)
  8. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)
  9. Darmawahyuni, Annisa; Nurmaini, Siti; Sukemi; Caesarendra, Wahyu; Bhayyu, Vicko; Rachmatullah, M. Naufal; Firdaus: Deep learning with a recurrent network structure in the sequence modeling of imbalanced data for ECG-rhythm classifier (2019)
  10. El Haouij, Neska; Poggi, Jean-Michel; Ghozi, Raja; Sevestre-Ghalila, Sylvie; Jaïdane, Mériem: Random forest-based approach for physiological functional variable selection for driver’s stress level classification (2019)
  11. Khojandi, Anahita; Shylo, Oleg; Zokaeinikoo, Maryam: Automatic EEG classification: a path to smart and connected sleep interventions (2019)
  12. Kiefer, Nicholas; Oremek, Maximilian J.; Hoeft, Andreas; Zenker, Sven: Model-based quantification of left ventricular diastolic function in critically ill patients with atrial fibrillation from routine data: a feasibility study (2019)
  13. Kovács, Péter; Fekete, Andrea M.: Nonlinear least-squares spline fitting with variable knots (2019)
  14. Li, Bao; Wang, Wenxin; Mao, Boyan; Liu, Youjun: A method to personalize the lumped parameter model of coronary artery (2019)
  15. Minati, Ludovico; Yoshimura, Natsue; Frasca, Mattia; Drożdż, Stanisław; Koike, Yasuharu: Warped phase coherence: an empirical synchronization measure combining phase and amplitude information (2019)
  16. Ni, Weiguang; Qi, Jianzhuo; Liu, Lijia; Li, Suyi: A pulse signal preprocessing method based on the Chauvenet criterion (2019)
  17. Shang, Du; Shang, Pengjian; Liu, Liu: Multidimensional scaling method for complex time series feature classification based on generalized complexity-invariant distance (2019)
  18. Titus, Geevarghese; Sudhakar, M. S.: Context adaptive residual coding for efficient compression of MCEEG employing wave atom transforms (2019)
  19. Tucker, J. Derek; Lewis, John R.; Srivastava, Anuj: Elastic functional principal component regression (2019)
  20. van Gent, P., Farah, H., van Nes, N. and van Arem, B.: Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project (2019) not zbMATH

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