Related Experiment Video
Updated: Jan 2, 2026
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram
Zhi Li1, Dengshi Zhou2, Li Wan3
1College of Electronic and Information Engineering, Sichuan University, Chengdu 610065, China; Key Laboratory of Wireless Power Transmission of Ministry of Education, Sichuan University, 610065, China.
Insights
A novel deep learning model accurately classifies cardiac arrhythmia using electrocardiogram (ECG) data. This advanced ResNet algorithm aids clinicians in diagnosing heart conditions and reducing mortality.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Electrocardiogram (ECG) is a simple, non-invasive tool for diagnosing heart disease, including various types of arrhythmia.
- Accurate arrhythmia detection is crucial for preventing heart disease progression and reducing mortality.
Purpose of the Study:
- To develop a novel deep learning method for classifying cardiac arrhythmia using ECG signals.
- To evaluate the performance of a deep residual network (ResNet) for automated heartbeat identification.
Main Methods:
- A 31-layer, one-dimensional (1D) residual convolutional neural network (ResNet) was developed.
- The algorithm incorporates residual blocks with 1D convolution, batch normalization, ReLU activation, and identity shortcut connections.
- Two-lead ECG signals were utilized in combination with the deep learning model to identify five distinct heartbeat types.
Main Results:
- The deep ResNet model achieved high classification performance on single-lead ECG data, with an average accuracy of 99.06%, sensitivity of 93.21%, and positive predictivity of 96.76%.
- On 2-lead ECG datasets, the model demonstrated excellent results, achieving 99.38% accuracy, 94.54% sensitivity, and 98.14% specificity.
- The proposed method shows significant potential for automated cardiac arrhythmia detection.
Conclusions:
- The developed deep learning method, based on ResNet, effectively classifies cardiac arrhythmia using ECG data.
- This approach can serve as a valuable adjunct tool to assist clinicians in diagnosing heart conditions.
Background:
The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be classified into many types, including life-threatening and non-life-threatening. Accurate detection of arrhythmic types can effectively prevent heart disease and reduce mortality.
Methods:
In this study, a novel deep learning method for classification of cardiac arrhythmia according to deep residual network (ResNet) is presented. We developed a 31-layer one-dimensional (1D) residual convolutional neural network. The algorithm includes four residual blocks, each of which consists of three 1D convolution layers, three batch normalization (BP) layers, three rectified linear unit (ReLU) layers, and an "identity shortcut connections" structure. In addition, we propose to use 2-lead ECG signals in combination with deep learning methods to automatically identify five different types of heartbeats.
Results:
We have obtained an average accuracy, sensitivity and positive predictivity of 99.06%, 93.21% and 96.76% respectively for single-lead ECG heartbeats. In the 2-lead datasets, the results show that the deep ResNet model has high classification performance, achieving an accuracy of 99.38%, sensitivity of 94.54%, and specificity of 98.14%.
Conclusion:
The proposed method can be used as an adjunct tool to assist clinicians in their diagnosis.
More Related Videos
Related Concept Videos
01:28Electrocardiogram Fundamentals
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
01:25Correlation between ECG and 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...
01:19Electrophysiology of Normal Cardiac Rhythm
01:24ECG Interpretation of Rhythms
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
01:30Cardiac Action Potential
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials