Classification of Electrocardiogram Signal Using Hybrid Deep Learning Techniques
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-5868-7_29
Reference20 articles.
1. Shi H, Qin C, Xiao D, Zhao L, Liu C (2020) Automated heartbeat classification based on deep neural network with multiple input layers. Knowl-Based Syst 188:105036
2. Pandey SK, Janghel RR (2021) Automated detection of arrhythmia from electrocardiogram signal based on new convolutional encoded features with bidirectional long short-term memory network classifier. Phys Eng Sci Med 44(1):173–182
3. Li J (2018) Detection of premature ventricular contractions using densely connected deep convolutional neural network with spatial pyramid pooling layer. arXiv preprint arXiv:1806.04564
4. Pandey SK, Janghel RR (2021) Classification of electrocardiogram signal using an ensemble of deep learning models. Data Technol Appl 55:446–460
5. Pandey SK, Janghel RR (2019) Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Austr Phys Eng Sci Med 42(4):1129–1139
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