Related Experiment Video
Updated: Jan 18, 2026
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction
Hossein Khezripour1, Saadat Pour Mozaffari2, Midia Reshadi1
1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Insights
This study introduces a novel deep learning method for classifying cardiac arrhythmias using electrocardiogram (ECG) signals. The approach achieved high accuracy in identifying various heart rhythm conditions, aiding in timely diagnosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Cardiac arrhythmias require timely diagnosis for effective treatment.
- Electrocardiogram (ECG) signals are crucial for identifying heart rhythm abnormalities.
- Accurate classification of diverse arrhythmias remains a challenge in medical research.
Purpose of the Study:
- To develop and evaluate a new deep learning-based method for classifying cardiac arrhythmias from ECG signals.
- To enhance the sensitivity and accuracy of ECG signal classification for various heart conditions.
- To diagnose heart rhythm diseases by classifying ECG signals into distinct rhythm classes.
Main Methods:
- ECG signals were preprocessed using noise removal filters.
- Discrete Wavelet Transform (DWT) was employed for feature extraction based on wavelet decomposition energy and PQRS morphological features.
- Genetic algorithms were utilized to reduce feature vectors and optimize Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) weights.
- A deep learning framework was implemented for signal classification.
Main Results:
- The proposed method achieved high learning accuracy: 99.9% for ANN training and 88.92% for ANN testing.
- ANFIS demonstrated comparable accuracy: 99.8% for training and 88.83% for testing.
- The classification successfully distinguished between normal heartbeats and various arrhythmias, including congestive heart failure, ventricular arrhythmias, atrial fibrillation, and atrial flutter.
Conclusions:
- The developed deep learning approach shows significant promise for accurate cardiac arrhythmia classification using ECG signals.
- The method offers a sensitive and effective tool for the diagnosis of heart rhythm diseases.
- The combination of DWT, genetic algorithms, and deep learning models (ANN, ANFIS) provides a robust framework for medical signal analysis.
Abstract:
Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.
Related Concept Videos
01:30Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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...
01:25Correlation 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...