Key result
PSO-optimized CNN-LSTM and CNN-GRU models achieved up to 99.96% accuracy for myocardial infarction detection and 99.87% for localization, outperforming state-of-the-art methods.
Why the study?
Traditional electrocardiogram detection algorithms for myocardial infarction rely on hand-crafted characteristics and demand high subject knowledge, necessitating automated deep learning models.
Do PSO-optimized hybrid deep learning models (CNN-LSTM and CNN-GRU) improve the accuracy of myocardial infarction detection and localization using ECG data?
Do PSO-optimized hybrid deep learning models (CNN-LSTM and CNN-GRU) improve the accuracy of myocardial infarction detection and localization using ECG data?
PSO-optimized hybrid deep learning models demonstrate exceptionally high accuracy for the automated detection and localization of myocardial infarction using standard 12-lead ECG data.
Hybrid DL models may aid automated ECG-based MI detection; hypothesis-generating pending prospective validation.
Myocardial infarction (MI) damages the heart muscles irreversibly and can result in death. Hence, it is essential to diagnose MI early to save lives. However, traditional detection algorithms using an electrocardiogram (ECG) rely mainly on hand-crafted characteristics and demand a high level of subject knowledge. Hence, two hybrid deep learning (DL) models, i.e., convolutional neural network (CNN) with long short-term memory (LSTM) and CNN with the gated recurrent unit (GRU) are proposed for classifying MI. Particle swarm optimization (PSO) is employed to optimize the hyperparameters of the hybrid models for optimal performance. The hybrid models’ simulations are performed using intra- and inter-patient data from the Physikalisch-Technische Bundesanstalt (PTB)-ECG database in Python for all standard 12 leads individually for the detection and localization of MI. Among MI, normal, and non-MI, the MI is detected by employing the optimized CNN-LSTM on intra-patient data from lead-V2 that provides an accuracy of 99.96%. In contrast, the optimized CNN-GRU method offers an accuracy of 90.15% on inter-patient data from lead-V5 for MI detection. For MI localization, the optimized CNN-LSTM model on intra-patient lead-V1 data provides an accuracy of 99.87% to classify 11 classes. On the other hand, CNN-GRU provides an accuracy of 66.32% to classify five types of MI on inter-patient lead-V1 data. This study and analysis conclude that this article’s two proposed hybrid models outperform state-of-the-art methods on chest leads.
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Sahu et al. (2024) studied Myocardial infarction. PSO optimized hybrid deep learning models (CNN-LSTM and CNN-GRU) vs. State-of-the-art methods was evaluated on Detection and localization of myocardial infarction. PSO-optimized CNN-LSTM and CNN-GRU models achieved up to 99.96% accuracy for myocardial infarction detection and 99.87% for localization, outperforming state-of-the-art methods.
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