Automated detection of Myocardial Infarction (MI) using ECG signals with artificial intelligence
Myocardial Infarction (MI) is known as heart attack, it is one of the most life-threatening cardiovascular diseases. During infarction, the coronary artery which is responsible for the delivery of blood, oxygen and nutrients, is fully or partially blocked, and the heart muscle will die of ischemi...
| Main Author: | |
|---|---|
| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
|
| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6981/ http://eprints.utar.edu.my/6981/1/fyp_CS_2024_KZK.pdf |
| Summary: | Myocardial Infarction (MI) is known as heart attack, it is one of the most life-threatening
cardiovascular diseases. During infarction, the coronary artery which is responsible for the
delivery of blood, oxygen and nutrients, is fully or partially blocked, and the heart muscle
will die of ischemia. Percutaneous Coronary Intervention (PCI) is a nonsurgical technique to
treat MI, the faster the patient receives PCI treatment, the higher the survival rate. The heart
activity (pumping blood) is controlled by the electrical current generated by itself, therefore
12-lead electrocardiogram is an excellent tool to capture the activity of the heart, the pattern
of a complete heart cycle is referred to as the PQRST cycle. MI will cause a morphological
change to this pattern, therefore this can be used to diagnose the MI. In order to avoid the
intra-/inter-observer effect caused by manual human interpretation, many researchers
proposed machine-learning-based methods and then nowadays many deep-learning-based
methods have emerged to perform automatic and end-to-end classification. Nevertheless,
many studies that emphasized deep learning models did not care about the data split method
during their experiment, this led to a misleadingly supreme performance due to information
leakage problem. The models might be trained to memorize which subjects have MI
heartbeats instead of learning the features related to the disease itself from the amplitude and
time (in a sequential model). Thus, this research proposed three models: Long Short-Term
Memory (LSTM), Gated Recurrent Unit (GRU) and 1-dimensional Convolutional Neural
Network (1D-CNN) with the implementation of intra-patient and inter-patient data split
techniques. In FYP 2, the architecture for each model had been improved compared to FYP 1
to augment the performance, then regularization and dropout techniques were applied to
increase the generalization ability and finally, one transformer model had been developed to
test its potential in processing ECG signal. From the perspective of the inter-patient method,
the LSTM model obtained 90.53% accuracy, while the 1D-CNN and GRU models obtained
85.82% and 86.65% accuracy respectively. On the other hand, for all the intra-patient models,
LSTM and GRU obtained a similar 95.4% accuracy while 1D-CNN obtained a 97.68%
accuracy. The transformer model achieved 82.28% and 91.15% in intra-patient and interpatient
analysis. Obviously, this has proven that the intra-patient models can produce a
misleadingly high result. Another dataset is obtained from the open database on the Internet,
but unfortunately, the testing result has shown that all of the models failed to generalize. |
|---|