[go: up one dir, main page]

Clemson et al., 2014 - Google Patents

Inverse approach to chronotaxic systems for single-variable time series

Clemson et al., 2014

View PDF
Document ID
8047444921073020723
Author
Clemson P
Suprunenko Y
Stankovski T
Stefanovska A
Publication year
Publication venue
Physical Review E

External Links

Snippet

Following the development of a new class of self-sustained oscillators with a time-varying but stable frequency, the inverse approach to these systems is now formulated. We show how observed data arranged in a single-variable time series can be used to recognize such …
Continue reading at link.aps.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • G06K9/00503Preprocessing, e.g. filtering
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7239Details of waveform analysis using differentiation including higher order derivatives
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7253Details of waveform analysis characterised by using transforms
    • A61B5/726Details of waveform analysis characterised by using transforms using Wavelet transforms
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • G06K9/00536Classification; Matching
    • G06K9/0055Classification; Matching by matching signal segments

Similar Documents

Publication Publication Date Title
Clemson et al. Inverse approach to chronotaxic systems for single-variable time series
CN110236573B (en) Psychological stress state detection method and related device
Faes et al. Lag-specific transfer entropy as a tool to assess cardiovascular and cardiorespiratory information transfer
US20140323897A1 (en) System and method for estimating high time-frequency resolution eeg spectrograms to monitor patient state
Matsuda et al. Time series decomposition into oscillation components and phase estimation
McIntosh et al. Estimation of phase in EEG rhythms for real-time applications
CN106778594A (en) Mental imagery EEG signal identification method based on LMD entropys feature and LVQ neutral nets
US11638562B2 (en) Brain activity analysis method and apparatus thereof
Cassidy et al. Bayesian nonstationary autoregressive models for biomedical signal analysis
Aburakhia et al. On the intersection of signal processing and machine learning: A use case-driven analysis approach
Biswas et al. A peak synchronization measure for multiple signals
Hwang et al. Enhancing privacy-preserving personal identification through federated learning with multimodal vital signs data
CN118648883B (en) Dual-mode blood pressure calculation method, device, equipment and storage medium
Nguyen et al. Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother
Rodríguez et al. Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction
Karimian et al. Noise assessment framework for optimizing ecg key generation
Insani et al. Investigation Reinforcement learning method for R-Wave detection on Electrocardiogram signal
Dutta et al. On a novel model for ecg signals and its statistical properties
Kamel et al. The fundamentals of EEG signal processing
Baselli et al. Short and long term non-linear analysis of RR variability series
Chen et al. A fast ECG diagnosis using frequency-based compressive neural network
Nalwaya et al. Emotion identification based on EEG rhythms separated using improved eigenvalue decomposition of Hankel matrix
Rangarajan et al. Estimation of vector autoregressive parameters and granger causality from noisy multichannel data
JP2021094203A (en) Heart rate variation analysis device, method, and program
Wang et al. Blind source separation based on variational Bayesian independent component analysis