Myocardial Infarction Prediction Using Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-32029-3_13
Reference13 articles.
1. Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415, 190–198 (2017)
2. Aranda, A., Bonizzi, P., Karel, J., Peeters, R.: Performance of dower’s inverse transform and frank lead system for identification of myocardial infarction. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4495–4498. IEEE (2015)
3. Ardan, A., Ma’arif, M., Aisyah, Z., Olivia, M., Titin, S.: Myocardial infarction detection system from PTB diagnostic ECG database using fuzzy inference system for ST waves. In: Journal of Physics: Conference Series, vol. 1204, p. 012071. IOP Publishing (2019)
4. Baldi, P., Brunak, S., Frasconi, P., Soda, G., Pollastri, G.: Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15(11), 937–946 (1999)
5. Baloglu, U.B., Talo, M., Yildirim, O., San Tan, R., Acharya, U.R.: Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn. Lett. 122, 23–30 (2019)
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