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Updated: Aug 29, 2025
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep
Mohamed Hammad1, Samia Allaoua Chelloug2, Reem Alkanhel2
1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt.
Insights
This study introduces a novel deep learning approach for detecting myocardial infarction (MI) and conduction disorders (CDs) from ECG data. The method achieves high accuracy, offering an efficient solution for automated cardiac diagnostics.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Electrocardiograms (ECGs) are crucial for diagnosing heart conditions.
- Analyzing long-term ECGs is time-consuming and challenging for cardiologists.
- Automated tools are needed for efficient detection of critical cardiac events like myocardial infarction (MI) and conduction disorders (CDs).
Purpose of the Study:
- To propose a novel deep learning approach for detecting MI and CDs using large-scale PTB-XL ECG data.
- To address challenges associated with existing deep learning methods, including data variability and computational expense.
- To develop a computationally efficient and interpretable diagnostic tool for cardiac arrhythmias.
Main Methods:
- A new deep learning model was developed for feature extraction from ECG signals.
- A custom activation function was proposed to enhance model convergence.
- Extracted deep features were classified using a Support Vector Machine (SVM).
- The approach considered data filtering and addressed challenges from diverse datasets.
Main Results:
- The proposed method achieved a high overall accuracy of 99.20% for MI and CD detection.
- The combination of Convolutional Neural Networks (CNNs) for feature extraction and SVM for classification proved effective.
- The custom activation function demonstrated fast convergence properties.
Conclusions:
- The developed deep learning approach offers a promising and accurate solution for automated detection of MI and CDs.
- The method overcomes limitations of traditional deep learning models in ECG analysis.
- This work contributes to the advancement of computer-aided diagnosis in cardiology.
Abstract:
An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.
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