Sukemi, Sukemi (2019) Artikel paper proseding di IEEE: Deep Learning with Long Short-Term Memory for Enhancement Myocardial Infarction Classification. Deep Learning with Long Short-Term Memory for Enhancement Myocardial Infarction Classification. pp. 19-23. ISSN 2379-755X
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Abstract
Myocardial infarction (MI) may be a minor event in a type of chronic disease, even undetectable. However, it can also be a major disaster that causes sudden death. The multivariance in ECG signals for different patients causes the interpretation of existence MI is a difficult task. The various conventional method is proposed to diagnose MI of ECG signals. The conventional classifier algorithm uses a shallow feature learning architecture based on the hand-crafted feature. This paper is only a preliminary study so that this paper contains only brief analysis and plan. However, it can present other point-of-view to process cardiac rhythm that associated in timesteps based on deep learning approach. Basically, a shallow feature learns as well as deep learning. However, the advantage and characteristics of deep learning will make classifier learn automatically without having to involve human intervention. Long short-term memory (LSTM) as deep learning classifier is proposed to the binary classification of MI and healthy control patients. The public ECG signals dataset of Physionet is used to support our research. In the evaluation of binary classification, balanced accuracy (BAcc) and Matthew’s Correlation Coefficient (MCC) metrics are used to analyze imbalance sequential data of 4.57 Imbalance Ratio (IR). The overall, 3 hidden LSTM layers as classifier show good performance in imbalanced data to classify MI with precision, sensitivity, F1 score, BAcc, and MCC is 0.91, 0.91, 0.90, 0.83, and 0.75 respectively.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Dr. Sukemi Sukemi |
| Date Deposited: | 30 Dec 2021 03:12 |
| Last Modified: | 18 Jan 2022 06:01 |
| URI: | http://repository.unsri.ac.id/id/eprint/59972 |
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