Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

State Space Representation01:27

State Space Representation

643
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
643
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.8K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
13.8K
Energy and Power Signals01:17

Energy and Power Signals

1.3K
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
1.3K
State Space to Transfer Function01:21

State Space to Transfer Function

637
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
637
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

752
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
752

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework.

Npj imaging..·2025
Same author

Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing: Towards Data-Driven Decision-Making and Precise Medicine.

Diagnostics (Basel, Switzerland)·2025
Same author

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Computers in biology and medicine·2025
Same author

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Biomedical engineering letters·2024
Same author

Detection of Common Cold from Speech Signals using Deep Neural Network.

Circuits, systems, and signal processing·2022
Same author

Atrial Fibrillation Burden Estimation Using Multi-Task Deep Convolutional Neural Network.

IEEE journal of biomedical and health informatics·2022

Related Experiment Video

Updated: Mar 6, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K

Analysis of physiological signals using state space correlation entropy.

Rajesh Kumar Tripathy1, Suman Deb1, Samarendra Dandapat1

  • 1Department of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati 781039 , India.

Healthcare Technology Letters
|March 7, 2017
PubMed
Summary

Researchers developed a new state space correlation entropy (SSCE) for time series analysis. This novel entropy measure shows superior performance in detecting ventricular arrhythmia compared to existing methods.

Keywords:
ECGEEGSSCESVM classifiercorrelation methodselectrocardiographyelectroencephalographyentropymedical disordersmedical signal processingpermutation entropyphysiological signalsreal valued signalssample entropyshockable ventricular arrhythmiasignal classificationsignal reconstructionspeechspeech processingstate space correlation entropystate space reconstructionstate-space methodssupport vector machinesupport vector machinessynthetic valued signalstime series

More Related Videos

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.3K

Related Experiment Videos

Last Updated: Mar 6, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.3K

Area of Science:

  • * Signal processing and time series analysis.
  • * Biomedical engineering and arrhythmia detection.

Background:

  • * Accurate analysis of time series data is crucial in various scientific fields.
  • * Existing entropy measures may have limitations in capturing complex signal dynamics.
  • * Ventricular arrhythmia detection requires sensitive and reliable analytical tools.

Purpose of the Study:

  • * To introduce a novel entropy measure, state space correlation entropy (SSCE), for time series analysis.
  • * To evaluate the performance of SSCE in detecting shockable ventricular arrhythmia.
  • * To compare SSCE with established entropy measures like sample entropy and permutation entropy.

Main Methods:

  • * State space reconstruction was employed to derive embedding vectors from time series.
  • * State space correlation entropy (SSCE) was computed based on the probability of correlations among embedding vectors.
  • * The performance of SSCE was assessed using synthetic and real-valued signals, including electrocardiogram (ECG) data.
  • * Support Vector Machine (SVM) classifier was utilized in conjunction with SSCE features for arrhythmia detection.

Main Results:

  • * The proposed SSCE measure, when combined with an SVM classifier, achieved a sensitivity of 91.60% for detecting shockable ventricular arrhythmia.
  • * This performance surpasses the sensitivity achieved by using sample entropy and permutation entropy features.
  • * SSCE demonstrated effectiveness in analyzing both synthetic and real-world signals.

Conclusions:

  • * State space correlation entropy (SSCE) is a promising new measure for time series analysis.
  • * SSCE offers improved sensitivity for detecting shockable ventricular arrhythmia compared to existing entropy methods.
  • * The findings suggest SSCE's potential utility in clinical applications for cardiac monitoring.