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Updated: Jun 12, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease
Anand Pandey1, Ajeet Singh2, Prasanthi Boyapati3
1Department of Computer Science and Application, SSET, Sharda University, Greater Noida, India.
Insights
This study introduces an automated deep learning system for cardiovascular disease (CVD) detection using Electrocardiograms (ECG). The FFNN-CQNGT model achieves high accuracy, offering a promising tool for early CVD identification and patient care.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Heart disease is a leading global cause of death, necessitating timely diagnosis.
- Electrocardiograms (ECG) are vital for identifying cardiovascular issues.
- Deep learning (DL) shows potential for rapid ECG anomaly detection, but automatic CVD detection remains challenging.
Purpose of the Study:
- To enhance cardiovascular disease diagnosis by integrating symptom-based detection and ECG analysis.
- To develop a novel automated prediction method for improving ECG diagnostic accuracy.
- To leverage DL features with ECG properties for precise CVD identification.
Main Methods:
- A Feed Forward Neural Network (FFNN) model was developed for automated ECG diagnosis.
- Chaos theory and destruction analysis were used to combine DL features and ECG properties.
- The Constant-Q non-stationary Gabor transform (CQNGT) converted 1D ECG data to 2D images for FFNN processing.
Main Results:
- The FFNN-CQNGT system demonstrated superior performance compared to state-of-the-art methods.
- Achieved a precision of 94.89%, accuracy of 95.55%, specificity of 93.77%, and sensitivity of 93.99%.
- Exhibited computational efficiency with a processing time of 2.114 ms and MSE of 40.32%.
Conclusions:
- The developed automated ECG-based DL system (FFNN-CQNGT) facilitates early-stage cardiovascular disease identification.
- This system holds significant potential for improving patient care and public health outcomes.
- The study contributes a novel approach to automated CVD classification using advanced DL techniques.
Background:
Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Disease (CVD) often gets identified through ECGs. Deep learning (DL) garners attention in healthcare due to its potential in swiftly diagnosing ECG anomalies, crucial for patient monitoring. Conversely, automatic CVD detection from ECGs poses a challenging task, wherein rule-based diagnostic models usually achieve top-notch performance. These models encounter complications in supervision vast volumes of diverse data, demanding widespread analysis and medical capability to ensure precise CVD diagnosis.
Objective:
This study aims to enhance cardiovascular disease diagnosis by combining symptom-based detection and ECG analysis.
Methods:
To enhance these experiments, we built a novel automated prediction method based on a Feed Forward Neural Network (FFNN) model. The fundamental objective of our method is to develop the accuracy of ECG diagnosis. Our strategy employs chaos theory and destruction analysis to combine optimum deep learning features with a well-organized set of ECG properties. In addition, we use the constant-Q non-stationary Gabor transform (CQNGT) to convert one-dimensional ECG data into a two-dimensional picture. A pre-trained FFNN processes this image. To identify significant features from the FFNN output that correspond with the ECG data, we employ pairwise feature proximity.
Results:
According to experimental findings, the suggested system, FFNN-CQNGT, surpasses other state-of-the-art systems in terms of precision of 94.89%, computational efficiency of 2.114 ms, accuracy of 95.55%, specificity of 93.77%, and sensitivity of 93.99% and MSE 40.32%.
Conclusion:
Contributing an automated ECG-based DL system based on FFNN-CQNGT for early-stage cardiovascular disease identification and classification holds great potential for both patient care and public health.
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