Volume 83, Issue 3 (June 2025)
Tehran Univ Med J 2025, 83(3): 151-159 |
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Akhavan H, Rezaei F. Analysis of human electrocardiogram (ECG) signals using
machine learning techniques. Tehran Univ Med J 2025; 83 (3) :151-159
URL: http://tumj.tums.ac.ir/article-1-13544-en.html
URL: http://tumj.tums.ac.ir/article-1-13544-en.html
1- Department of Biophotonic, Physics Faculty, K. N. Toosi University of Technology, Tehran, Iran.
Abstract: (606 Views)
Background: An Electrocardiogram is a non-invasive method for receiving heart signals. Despite advances in imaging methods, the electrocardiogram still plays an important role remains a vital tool in the diagnosis of heart diseases. Analysis of electrocardiogram signals plays an important role in the early detection of heart diseases such as arrhythmias and heart attacks. Today, with the advancement of science and technology, computer methods have received more and more attention from doctors. In this study, machine learning methods were used to classify normal and abnormal heartbeats.
Methods: The data under study were extracted from a dataset called Heartbeat published on the Kaggle website. This dataset includes samples of audio ECG signals that are divided into healthy and unhealthy categories. First, the data were preprocessed and normalized to prepare them for input into the model. Then, temporal and frequency features were extracted from the signals. Next, a hybrid model consisting of one-dimensional convolutional layers was designed and trained. Also, by using the early stopping method, overfitting was prevented and the stability of the model was improved.
Results: In this study, it was shown that by using deep learning, especially using CNN and 1D Conv, an accuracy of 0.99% and a loss of 0.0350 for test data in detecting normal and abnormal heartbeats can be achieved. This model has the ability to analyze complex structures and temporal dynamics of ECG signals and is able to detect patterns related to cardiac disorders.
Conclusion: Today, the electrocardiogram has received more attention than ever before. Appropriate selection of the model, data standardization, and a qualitative range of data are among the factors of high accuracy in this study. This study can be an effective step in the development of intelligent systems for diagnosing cardiac disorders and can be used in medical applications, especially in the field of continuous patient monitoring.
Methods: The data under study were extracted from a dataset called Heartbeat published on the Kaggle website. This dataset includes samples of audio ECG signals that are divided into healthy and unhealthy categories. First, the data were preprocessed and normalized to prepare them for input into the model. Then, temporal and frequency features were extracted from the signals. Next, a hybrid model consisting of one-dimensional convolutional layers was designed and trained. Also, by using the early stopping method, overfitting was prevented and the stability of the model was improved.
Results: In this study, it was shown that by using deep learning, especially using CNN and 1D Conv, an accuracy of 0.99% and a loss of 0.0350 for test data in detecting normal and abnormal heartbeats can be achieved. This model has the ability to analyze complex structures and temporal dynamics of ECG signals and is able to detect patterns related to cardiac disorders.
Conclusion: Today, the electrocardiogram has received more attention than ever before. Appropriate selection of the model, data standardization, and a qualitative range of data are among the factors of high accuracy in this study. This study can be an effective step in the development of intelligent systems for diagnosing cardiac disorders and can be used in medical applications, especially in the field of continuous patient monitoring.
Type of Study: Original Article |
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