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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

110
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
110
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

100
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
100
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

234
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
234
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

275
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
275

You might also read

Related Articles

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

Sort by
Same author

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society·2026
Same author

How Laboratory Innovations Are Shaping the Future of Multiple Myeloma Care.

Cancers·2026
Same author

Social Media, Medical Students, and Patient Exposure: Perceptions and Attitudes.

The Linacre quarterly·2026
Same author

Predictors and prognostic impact of dynamic left ventricular outflow tract obstruction during dobutamine stress echocardiography.

The international journal of cardiovascular imaging·2026
Same author

A Plugin-Based Architecture for Integrating AI Services in an Open-Source PACS.

Journal of imaging informatics in medicine·2026
Same author

Self-Assessed Leadership Influence in Trauma Programs.

Journal of trauma nursing : the official journal of the Society of Trauma Nurses·2026
Same journal

Multi-Source Data and Knowledge Fusion via Deep Learning for Dynamical Systems: Applications to Spatiotemporal Cardiac Modeling.

IISE transactions on healthcare systems engineering·2025
Same journal

Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.

IISE transactions on healthcare systems engineering·2024
Same journal

Radiotherapy toxicity prediction using knowledge-constrained generalized linear model.

IISE transactions on healthcare systems engineering·2024
Same journal

Mapping the process of ICU care delivery to improve treatment decisions in acute respiratory failure.

IISE transactions on healthcare systems engineering·2024
Same journal

Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.

IISE transactions on healthcare systems engineering·2023
Same journal

Gradient Boosted Trees for Spatial Data and Its Application to Medical Imaging Data.

IISE transactions on healthcare systems engineering·2022
See all related articles

Related Experiment Video

Updated: Jul 18, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.2K

A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine.

Wesam Alramadeen1, Yu Ding1, Carlos Costa2

  • 1Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, USA 13902, USA.

IISE Transactions on Healthcare Systems Engineering
|August 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new sparse linear mixed model for predicting sleep disorder severity indicators from complex health data. The model accurately identifies key features, improving automated diagnosis in telemonitoring.

Keywords:
group lassolinear mixed modelmulti-source mixed-frequency datatelemonitoring

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.7K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.1K

Related Experiment Videos

Last Updated: Jul 18, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.2K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.7K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.1K

Area of Science:

  • Digital Health
  • Biostatistics
  • Cardiology

Background:

  • Digital health and telemonitoring generate vast, complex datasets.
  • Existing models struggle with multi-source, mixed-frequency health data.
  • Automated prediction of Disease Severity Indicators (DSIs) for sleep disorders is lacking.

Purpose of the Study:

  • To develop a rigorous prediction model for DSIs from multi-source, mixed-frequency data.
  • To address challenges in high-dimensional data for sleep disorder telemonitoring.
  • To enable automated monitoring and diagnosis of sleep disorders.

Main Methods:

  • Proposed a sparse linear mixed model using modified Cholesky decomposition and group lasso penalties.
  • Developed a novel Expectation Maximization (EM) algorithm integrated with Majorization Maximization (MM) for model estimation.
  • Applied the method to the SHHS dataset for sleep disorder telemonitoring and diagnosis.

Main Results:

  • Identified significant feature groups consistent with existing sleep disorder research.
  • The proposed method demonstrated superior prediction accuracy compared to benchmark approaches.
  • Successfully applied the model to real-world telemonitoring data for sleep disorder diagnosis.

Conclusions:

  • The developed sparse linear mixed model effectively predicts DSIs from complex health data.
  • This approach enhances automated sleep disorder monitoring and diagnosis.
  • The findings support the use of advanced statistical modeling in digital health applications.