Jove
Visualize
Contact Us

Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

1.3K
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...
1.3K
Special considerations while measuring pulse01:13

Special considerations while measuring pulse

884
Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
884
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

2.4K
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
2.4K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

311
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
311
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

1.9K
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
1.9K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.5K
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...
11.5K

You might also read

Related Articles

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

Sort by
Same author

Monitoring of respiration and cardiorespiratory interactions from multichannel seismocardiography signals.

Physical and engineering sciences in medicine·2025
Same author

A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals.

Scientific data·2025
Same author

Review of Electrohydraulic Actuators Inspired by the HASEL Actuator.

Biomimetics (Basel, Switzerland)·2025
Same author

A Flexible PVDF Sensor for Forcecardiography.

Sensors (Basel, Switzerland)·2025
Same author

Fully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects.

Physical and engineering sciences in medicine·2025
Same author

Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units.

Sensors (Basel, Switzerland)·2025
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 Experiment Video

Updated: Jan 9, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K

An Edge AI Approach for Low-Power, Real-Time Atrial Fibrillation Detection on Wearable Devices Based on Heartbeat

Eliana Cinotti1, Maria Gragnaniello1, Salvatore Parlato1

  • 1Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Naples, Italy.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study demonstrates that neural networks on microcontrollers can accurately detect atrial fibrillation (AF) using heart rhythm data. This enables real-time, low-power AF screening on wearable devices.

Keywords:
atrial fibrillationdiagnosisedge AIheart ratelow-powermicrocontrollerneural networkswearables

More Related Videos

A New Single Chamber Implantable Defibrillator with Atrial Sensing: A Practical Demonstration of Sensing and Ease of Implantation
16:40

A New Single Chamber Implantable Defibrillator with Atrial Sensing: A Practical Demonstration of Sensing and Ease of Implantation

Published on: February 28, 2012

26.7K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

1.5K

Related Experiment Videos

Last Updated: Jan 9, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K
A New Single Chamber Implantable Defibrillator with Atrial Sensing: A Practical Demonstration of Sensing and Ease of Implantation
16:40

A New Single Chamber Implantable Defibrillator with Atrial Sensing: A Practical Demonstration of Sensing and Ease of Implantation

Published on: February 28, 2012

26.7K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

1.5K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiology

Background:

  • Atrial fibrillation (AF) is a common heart rhythm disorder requiring early detection for effective treatment.
  • Wearable devices can capture heart rhythm signals, but complex algorithms for AF detection often demand significant computational resources.
  • Developing efficient algorithms for on-device AF detection is crucial for widespread personal health monitoring.

Purpose of the Study:

  • To investigate the feasibility of using neural network algorithms directly on microcontrollers for real-time atrial fibrillation detection.
  • To evaluate the performance of a custom 1D convolutional neural network (1D-CNN) for AF recognition using inter-beat intervals (RR).
  • To assess the performance and resource consumption of an Edge AI prototype for AF detection.

Main Methods:

  • Extracted RR sequences (25, 50, 100 intervals) from a public ECG database with annotated AF episodes.
  • Designed and validated a 1D-CNN using 5-fold subject-wise cross-validation for subject-independent evaluation.
  • Developed an Edge AI prototype with an ECG front-end, microcontroller, and IoT module for real-world testing.

Main Results:

  • The 1D-CNN achieved high test accuracies (up to 0.980 ± 0.023) for AF detection, with performance improving with longer RR sequences.
  • Subject-independent evaluation confirmed robust generalization, with high precision, recall, F1-score, and AUC-ROC.
  • The Edge AI prototype demonstrated real-time AF recognition with very low power consumption, suitable for wearable applications.

Conclusions:

  • Neural network algorithms can be effectively deployed on microcontrollers for real-time, low-power atrial fibrillation detection.
  • The proposed methodology offers a robust solution for personal AF screening using various physiological signals.
  • The system facilitates early AF detection and can transmit suspicious ECG traces for physician validation.