Key result
Hybrid deep learning models combining CNN and RNN architectures achieved an overall accuracy of 89% (CNN-LSTM) and 91% (CNN-BILSTM) for the automatic detection of myocardial infarction from ECGs.
Why the study?
Fast MI detection is critical to avoid treatment delay, but standard CNN models lack the capability to retain temporal ECG information.
Answers are grounded in this paper and cite their sources.
A hybrid CNN-BILSTM deep learning model achieved 91% accuracy in automatically detecting myocardial infarction from ECG readings.
Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model.
No takes yet. Share an insight, caveat, or question.
Hasbullah et al. (2023) studied Myocardial Infarction. Hybrid CNN-LSTM and CNN-BILSTM models was evaluated on Overall accuracy. Hybrid deep learning models combining CNN and RNN architectures achieved an overall accuracy of 89% (CNN-LSTM) and 91% (CNN-BILSTM) for the automatic detection of myocardial infarction from ECGs.
Synapse has enriched 10 related papers. Consider them for comparative context: