Key result
An image-based convolutional neural network for classifying cardiac cycles achieved an accuracy of 0.686, precision of 0.639, recall of 0.854, and f1-score of 0.731 on the MIT-BIH database.
Why the study?
Effective recognition of cardiac pathologies is urgently needed because doctors cannot always accurately identify pathological heart contractions due to high ECG signal variability across patients.
A convolutional neural network-based algorithm can classify cardiac cycles from ECG images with moderate accuracy and high recall.
Moderate accuracy on benchmark data is hypothesis-generating; leaves open prospective clinical validation.
One of the main methods for diagnosing cardiac arrhythmias is electrocardiography (ECG). It helps the doctor to understand the causes of various complaints of the patient and to prescribe treatment. There is an urgent need for effective recognition of cardiac pathologies in order to improve the treatment and prevention of cardiac arrhythmias. The doctor is not always able to accurately determine the pathological contractions of the heart due to the fact that the ECG signal is very variable and can be very different in different people with different cardiac disorders. In this paper, proposed an effective algorithm for classification of cardiac pathologies cycles based on images using convolutional neural network. MIT-BIH arrhythmia database was used for training and evaluation of the proposed method. 0.686 – Accuracy, 0.639 – precision, 0.854 – recall and 0.731 – f1-score metric values were achieved.
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Mahel et al. (2022) studied Cardiac arrhythmias. Convolutional neural network for classification of cardiac cycles based on images was evaluated on Classification performance metrics (accuracy, precision, recall, f1-score). An image-based convolutional neural network for classifying cardiac cycles achieved an accuracy of 0.686, precision of 0.639, recall of 0.854, and f1-score of 0.731 on the MIT-BIH database.
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