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

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

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

Sort by
Same author

The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.

Animals : an open access journal from MDPI·2026
Same author

Machine Learning-Based Risk Stratification for Sudden Cardiac Death Using Clinical and Device-Derived Data.

Sensors (Basel, Switzerland)·2026
Same author

Improving Risk Stratification in Sudden Cardiac Death Using Interpretable Machine Learning: A Clinical Perspective.

Healthcare (Basel, Switzerland)·2025
Same author

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Sensors (Basel, Switzerland)·2025
Same author

EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization.

Sensors (Basel, Switzerland)·2021
Same author

A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems.

Sensors (Basel, Switzerland)·2021

Related Experiment Video

Updated: Jun 27, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.4K

Intelligent Biosignal Analysis Methods.

Alan Jovic1

  • 1Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)
|July 24, 2021
PubMed
Summary

This editorial introduces accepted manuscripts on intelligent biosignal analysis methods. It highlights advancements in applying artificial intelligence to biosignal processing for enhanced health monitoring and diagnostics.

Area of Science:

  • Biosignal analysis
  • Artificial intelligence in healthcare
  • Sensors technology

Background:

  • This editorial introduces accepted manuscripts for a special issue on intelligent biosignal analysis methods.
  • The collection focuses on novel applications of artificial intelligence (AI) and machine learning (ML) in processing and interpreting complex biological signals.

Discussion:

  • The manuscripts cover a range of biosignals, including electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG).
  • Emphasis is placed on the development of sophisticated algorithms for real-time analysis and improved diagnostic accuracy.
  • The integration of AI/ML is crucial for extracting meaningful patterns from noisy and high-dimensional biosignal data.

Key Insights:

  • Accepted papers showcase innovative AI-driven techniques for biosignal interpretation.

More Related Videos

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.7K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.5K

Related Experiment Videos

Last Updated: Jun 27, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.4K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.7K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.5K
  • These methods offer potential for more accurate and efficient disease detection and patient monitoring.
  • The special issue highlights the growing importance of intelligent systems in biomedical engineering.

    • Outlook:

    • Future research directions include the development of more robust and interpretable AI models for biosignals.
    • The application of these intelligent methods is expected to expand across various medical fields.
    • Continued advancements in sensor technology will further enable sophisticated biosignal analysis.