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

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

You might also read

Related Articles

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

Sort by

Same author

Engineering and experimental evaluation of a smart wireless glove for gamified upper limb rehabilitation in Parkinson's disease.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same author

Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition.

Sensors (Basel, Switzerland)·2026

Same author

Exploring the Feasibility of Fall Detection Using Bluetooth Low Energy Channel Sounding in Residential Environments.

Sensors (Basel, Switzerland)·2026

Same author

Cardiac Organ Damage in Young Adults with Cryptogenic Ischaemic Stroke: The SECRETO Study.

High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension·2026

Same author

Preceding Infections and Coagulation Biomarkers in Early-Onset Cryptogenic Ischemic Stroke.

Stroke·2026

Same author

Thrombophilia Screening in Young Patients With Cryptogenic Ischemic Stroke.

Stroke·2026

Same journal

Correction: Haddock et al. <i>Imagine the Possibilities Pain Coalition</i> and Opioid Marketing to Veterans: Lessons for Military and Veterans Healthcare. <i>Healthcare</i> 2025, <i>13</i>, 434.

Healthcare (Basel, Switzerland)·2026

Same journal

Macro Responsibility in the Microvascular World: Nurse Experiences in Flap Care, a Phenomenological Study.

Healthcare (Basel, Switzerland)·2026

Same journal

Agreement Between Standing Eight-Point Multifrequency Bioelectrical Impedance Analysis and Dual-Energy X-Ray Absorptiometry for Body Composition Assessment in Apparently Healthy Greek Adults.

Healthcare (Basel, Switzerland)·2026

Same journal

'It's Not About the Food'-Understanding the Lived Experience of Patients Who Developed Hospital-Acquired Malnutrition (HAM) and That of Their Carers.

Healthcare (Basel, Switzerland)·2026

Same journal

Unveiling the Humanizing and Therapeutic Values of Live Music in Healthcare Settings: A Scoping Review.

Healthcare (Basel, Switzerland)·2026

Same journal

Respiratory Rehabilitation and Decannulation in Adults with Prolonged Mechanical Ventilation After Tracheostomy: A Narrative Review.

Healthcare (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2025

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

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

Published on: July 24, 2019

Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine

Viroslava Kapustynska¹, Vytautas Abromavičius¹, Artūras Serackis¹

¹Faculty of Electronics, Vilnius Gediminas Technical University, Plytinės st. 25, 10105 Vilnius, Lithuania.

Healthcare (Basel, Switzerland)

|September 14, 2024

Summary

This summary is machine-generated.

Wearable biosensors detect autonomic nervous system changes during sleep, aiding early migraine prediction. Electrodermal activity and skin temperature are key indicators for identifying pre-migraine patterns.

Keywords:

feature ranking machine learning migraine prediction nocturnal monitoring signal feature extraction sleep analysis wearable biosensors

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Related Experiment Videos

Last Updated: Jun 13, 2025

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

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

Published on: July 24, 2019

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Area of Science:

Neurology
Biomedical Engineering
Data Science

Background:

Migraine is a prevalent neurological disorder causing severe headaches.
Autonomic nervous system (ANS) dysfunction is observed during migraine attacks.
Understanding pre-migraine physiological changes is crucial for early detection.

Purpose of the Study:

To investigate autonomic nervous system (ANS) alterations during the pre-migraine night.
To utilize wearable biosensor data for predicting migraine occurrences.
To identify significant physiological features for early migraine detection.

Main Methods:

Ten individuals monitored using wearable biosensors during sleep.
Analysis of physiological, activity-based, and signal processing metrics.
Machine learning models (XGBoost, Random Forest, etc.) trained on nocturnal data with varying analysis frame durations (5-120 min).

Main Results:

Electrodermal activity, skin temperature, and accelerometer data showed significant predictive value in shorter analysis frames (5-10 min).
Analysis of variance (ANOVA) identified key distinguishing features between pre-migraine and migraine-free nights.
An XGBoost model with a 5-min frame achieved 0.806 accuracy, 0.638 precision, 0.595 recall, and 0.607 F1-score.

Conclusions:

Specific ANS features, particularly electrodermal activity and skin temperature, can indicate an impending migraine.
Short analysis frames (5-10 min) are most effective for identifying pre-migraine signatures.
While promising, further model refinement is needed for clinical application in migraine prediction.