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

Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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...
Dysrhythmias VI: Management of Dysrhythmias01:25

Dysrhythmias VI: Management of Dysrhythmias

Dysrhythmia management involves a multifaceted approach, incorporating pharmacological treatments, medical procedures, surgical interventions, lifestyle modifications, and patient education.Pharmacological ManagementAntiarrhythmic Drugs:Class I (Sodium Channel Blockers): This class includes quinidine and procainamide, which reduce the speed of impulse conduction in the heart, stabilize the cardiac membrane, and control arrhythmias. Quinidine and procainamide are Class IA agents that prolong the...
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
Pulse rhythm01:30

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 muscle...
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.

You might also read

Related Articles

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

Sort by
Same author

Heart Sound Classification with MFCCs and Wavelet Daubechies Analysis Using Machine Learning Algorithms.

Diagnostics (Basel, Switzerland)·2026
Same author

Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography.

Diagnostics (Basel, Switzerland)·2025
Same author

Convolutional Neural Network for Depression and Schizophrenia Detection.

Diagnostics (Basel, Switzerland)·2025
Same author

Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation.

Diseases (Basel, Switzerland)·2023
Same author

Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2023
Same author

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms.

Sensors (Basel, Switzerland)·2023
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for

Misael Zambrano-de la Torre1, Sebastian Guzman-Alfaro1, Andrea Acuña-Correa2

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study demonstrates a real-time cardiac arrhythmia classification system using a quantized deep learning model on an Arduino microcontroller. The Tiny Machine Learning (TinyML) system achieves high accuracy with minimal resources, enabling low-cost cardiac monitoring.

Keywords:
ECGTinyMLarrhythmia classificationedge AIembedded systemslow-cost healthcare

Related Experiment Videos

Last Updated: May 28, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Area of Science:

  • Embedded Systems Engineering
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Edge computing and Tiny Machine Learning (TinyML) enable AI on resource-constrained devices.
  • Deploying AI on microcontrollers requires efficient models and hardware optimization.
  • Cardiac arrhythmia detection is crucial for remote and low-cost healthcare solutions.

Purpose of the Study:

  • To design, implement, and validate a real-time cardiac arrhythmia classification system.
  • To deploy a quantized 1D-CNN model on an 8-bit Arduino UNO microcontroller.
  • To demonstrate the feasibility of TinyML for low-cost cardiac monitoring.

Main Methods:

  • ECG signal acquisition using AD8232, signal preprocessing, and heartbeat segmentation.
  • Development and quantization of a 1D-CNN model using TensorFlow and TensorFlow Lite.
  • Deployment on an Arduino UNO with real-time visualization on an OLED display.

Main Results:

  • Achieved 97.6% accuracy in classifying Normal, Ventricular, and Supraventricular arrhythmias.
  • Model footprint below 24 KB with an average inference time of 200 ms per heartbeat.
  • Successfully enabled real-time operation on a resource-constrained microcontroller.

Conclusions:

  • Lightweight deep learning models can be deployed on ultra-constrained embedded systems via TinyML.
  • The system serves as a proof-of-concept for low-cost cardiac monitoring technologies.
  • Highlights the trade-offs between AI performance and hardware limitations in embedded systems.