Published in , 2024
Publications
Classification of Arrhythmias with Inter and Intra Patient Paradigms Using Deep Learning
Published in Journal of Health Informatics, 2021
Classification of cardiac arrhythmia from the morphology of an electrocardiogram signal (ECG) using Deep Learning techniques. Method: We propose a two-level hierarchical classifier. The first level classifies normal and abnormal heartbeats, and the second deals with the multi class identification of the abnormal beats. The classifier is a convolutional neural network architecture (CNN) employed in feature extraction and classification of ECG signal. Results: The method was evaluated on the MIT dataset on the intra-patient and inter-patient paradigms, achieving average accuracy of 98.52% and 90.39%, respectively. Conclusion: The hierarchical method shows improvement on the classification of cardiac arrhythmia, especially on the minority classes, in both paradigms
Recommended citation: Passo, Sthefanie Jofer Gomes. (2021). "Classification of Arrhythmias with Inter and Intra Patient Paradigms Using Deep Learning." Journal of Health Informatics. http://academicpages.github.io/files/Classification-of-Arrhythmias-with-Inter-and-Intra-Patient-Paradigms-Using-Deep-Learning.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. http://academicpages.github.io/files/paper1.pdf