Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Signals

Classification of Signals

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...

You might also read

Related Articles

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

Sort by

Same author

Machine learning-based analysis of the relationship between brain lesion sites and swallowing and cognitive functions in stroke patients.

Scientific reports·2026

Same author

Finger motor skills and related brain regions in patients with cognitive disorder.

Journal of Alzheimer's disease : JAD·2025

Same author

Differences in Finger Dexterity in Patients With Mild and Moderate Alzheimer's Disease-A Study of Cognitive Function by Disease Severity.

Brain and behavior·2025

Same author

Detecting mild cognitive impairment by applying integrated random forest to finger tapping.

Medical & biological engineering & computing·2025

Same author

Cognitive screening test for rehabilitation using spatiotemporal data extracted from a digital trail making test part-A.

Heliyon·2024

Same author

The osteoclastic activity in apical distal region of molar mesial roots affects orthodontic tooth movement and root resorption in rats.

International journal of oral science·2024

Search research articles

Related Experiment Video

Updated: Dec 30, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Detection of Abnormal Segments in Finger Tapping Waveform using One-class SVM.

Yuko Sano, Ying Yin, Tomohiko Mizuguchi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 18, 2020

Summary

A new machine learning method objectively detects abnormal finger-tapping patterns for early dementia detection. This approach using the UB2 device shows promise in identifying subtle waveform changes missed by human visual assessment.

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Related Experiment Videos

Last Updated: Dec 30, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Area of Science:

Neurology
Biomedical Engineering
Machine Learning

Background:

Early dementia detection is crucial for timely intervention.
Traditional assessment methods can be subjective and may miss subtle indicators.
Finger-tapping analysis offers a non-invasive method for evaluating motor function.

Purpose of the Study:

To develop an objective, machine learning-based method for detecting abnormal finger-tapping waveforms.
To compare the machine learning method's accuracy against human visual assessment.
To identify specific features indicative of abnormality in finger-tapping patterns.

Main Methods:

Developed the UB2 finger-tapping device with magnetic sensors.
Collected 15-second right-hand finger tapping waveforms from 228 healthy volunteers.
Extracted 15 features from one-cycle taps and applied a one-class support vector machine (SVM).

Main Results:

The SVM identified 1032 abnormal one-cycle taps (8.0%) out of 12,898.
Abnormalities were linked to 'freezing' (fluctuations) and tap interval variations.
Machine learning detected subtle abnormalities missed by visual assessment, which sometimes overestimated or underestimated certain features.

Conclusions:

The developed machine learning method provides an objective approach to analyzing finger-tapping waveforms for early dementia detection.
This method shows potential for improving the accuracy and consistency of identifying early signs of cognitive decline.
The UB2 device combined with machine learning offers a promising tool for neurological assessments.