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

Holter Monitor: 24-Hour Monitoring

Holter Monitor: 24-Hour Monitoring

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...

Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

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...

Dysrhythmias V: Evaluating Dysrhythmias

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 II: Classification of Tachyarrhythmias

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...

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...

Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...

You might also read

Related Articles

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

Sort by

Same author

Incidence and Predictors of Acute Respiratory Distress Syndrome in Patients With Sepsis: A Two-Year Prospective Cohort Study From Khyber Pakhtunkhwa, Pakistan.

Cureus·2026

Same author

Bilateral Pulmonary Embolism With Bi-atrial Cardiac Thrombi Entrapped in a Patent Foramen Ovale.

Journal of community hospital internal medicine perspectives·2026

Same author

Using AI Algorithms and Machine Learning in the Analysis of a Bio-Purification Method (Therapeutic Emesis, Known as "Vamana Karma"): Protocol for a Mixed Methods Study.

JMIR research protocols·2026

Same author

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases.

Cell systems·2025

Same author

Clozapine-Induced Toxic Megacolon: A Rare but Serious Complication.

Cureus·2025

Same author

A narrative review of epicardial adipose tissue as a predictor of cardiometabolic risk in rheumatoid arthritis patients.

Annals of translational medicine·2025

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Videos

ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.

Sonam Nagar¹, Karan Verma¹, Sachin Singh¹

¹National Institute of Technology, Delhi, New Delhi, India.

Scientific Reports

|June 3, 2026

Summary

This summary is machine-generated.

ScaHybNet, a deep learning model, accurately classifies heart arrhythmias using ECG data. This advanced tool aids in preventing sudden cardiac death by improving arrhythmia detection, especially with noisy and imbalanced datasets.

Keywords:

Arrhythmia classification Continuous wavelet transform Multi-head self-attention ScaHybNet Scalogram imaging Transformer architecture

Related Experiment Videos

Area of Science:

Cardiology
Artificial Intelligence
Biomedical Signal Processing

Background:

Cardiovascular diseases are a leading global cause of mortality.
Accurate and timely detection of cardiac arrhythmias is crucial for preventing sudden cardiac death.
Existing methods may struggle with noisy and imbalanced electrocardiogram (ECG) data.

Purpose of the Study:

To propose ScaHybNet, a novel deep learning ensemble model for multi-class arrhythmia classification.
To enhance the accuracy and robustness of arrhythmia detection using ECG signals.
To address challenges posed by class imbalance and noise in ECG datasets.

Main Methods:

ECG signals were transformed into 224x224 RGB-scalogram images using Continuous Wavelet Transform (CWT) with a Morlet wavelet.
A hybrid deep learning architecture combining a Convolutional Neural Network (CNN) with residual blocks, a Bidirectional Long Short-Term Memory (BiLSTM) layer, and a Transformer encoder was developed.
Stratified balancing and inverse-frequency class weighting were employed to mitigate extreme class imbalance.
Fivefold cross-validation was used to assess model robustness.

Main Results:

ScaHybNet achieved an ensemble training accuracy of 99.81%.
The mean accuracy across fivefold cross-validation was 90.42% ± 1.26%.
On the unseen test set, the model demonstrated an ensemble test accuracy of 94.73%, with a precision of 76.51%, recall of 82.93%, and F1-score of 77.40%.

Conclusions:

ScaHybNet effectively classifies cardiac arrhythmias, showing superior or comparable performance to state-of-the-art methods, particularly with noisy and imbalanced ECG data.
The model's hybrid architecture, incorporating CNN, BiLSTM, and Transformer components, proves effective in learning spatial, temporal, and long-term dependencies.
ScaHybNet holds significant potential as a patient-centric tool to benefit the medical field in cardiovascular disease management.