Automated Diagnosis of Heart Arrhythmia Using Recurrent Neural Network
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Naser Safdarian, Nader Jafarnia, and GholamrezaAttarodi. (2014) “A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal.” Journal of Biomedical Science and Engineering 7(10):818-824.
Sharma L.N., Tripathy R.K., SamarendraDandapat. (2015) "Multiscale energy and eigenspace approach to detection and localization of myocardial infarction." IEEE Transactions on Biomedical engineering62(7):1827-37.
Rajendra Acharya U., Hamildo Fujita, Vidya K Sudarshan, Shu Lih Oh, Muhammad Adam, et al. (2016) “Automated detection and localization of myocardial infarction using electrocardiogram: A comparative study of different leads.” Knowledge-Based Systems99: 146-156.
Rajendra Acharya U., Hamildo Fujita, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, and Muhammad Adam. (2017) “Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals.” Information Sciences415: 190-198.
Tahsin Reasat and Celia Shahnaz. (2017) “Detection of inferior myocardial infarction using shallow convolutional neural networks.” arXiv preprint arXiv:1710.01115.
Babak Mohammad Zadeh-Asl and SeyedKamaledinSetarehdan. (2006) “Neural network-based arrhythmia classification using Heart Rate Variability signal.” In 14th European Signal Processing Conference pages 1-4.
Abhinav-Vishwa, Lal M.K., Dixit S, and Vardwai P. (2011) “Classification of arrhythmic ECG data using machine learning techniques.” International Journal of Interactive Multimedia and Artificial Intelligence1(4):67-70.