IOT BASED ECG: HYBRID CNN-BILSTM APPROACH FOR MYOCARDIAL INFARCTION CLASSIFICATION

Abdelmalek Makhir; My Hachem El Yousfi Alaoui; Larbi Bellarbi; Abdelilah Jilbab

doi:10.35784/iapgos.6045

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Published: Sep 30, 2024

DOI: https://doi.org/10.35784/iapgos.6045

Issue Vol. 14 No. 3 (2024)

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IOT BASED ECG: HYBRID CNN-BILSTM APPROACH FOR MYOCARDIAL INFARCTION CLASSIFICATION
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DOI

https://doi.org/10.35784/iapgos.6045

Authors

Abdelmalek Makhir

abdelmalek_makhir@um5.ac.ma

Mohammed V University in Rabat, Morocco

https://orcid.org/0009-0001-0545-818X

My Hachem El Yousfi Alaoui

h.elyousfi@um5r.ac.ma

Mohammed V University in Rabat, Morocco

Larbi Bellarbi

l.bellarbi@um5r.ac.ma

Mohammed V University in Rabat, Morocco

Abdelilah Jilbab

a_jilbab@yahoo.fr

Mohammed V University in Rabat, Morocco

Abstract

Cardiovascular disease such as ischemic heart disease and stroke are the most dangerous diseases in the WHO stats. Myocardial Infarction (MI), an ischemic disease of the heart, occurs due to a sudden blockage in the coronary arteries that supply blood to the heart causing a lack of oxygen and nutrients. The MI patient needs continuous monitoring using electrocardiography, the latter is always at risk of developing complications such as arrhythmias. As a solution, we proposed an internet of things (IoT) based ECG system for monitoring, the application layer was reserved for the detection of MI and arrhythmias using artificial intelligence so that the patients can keep being monitored even outside health facilities. For this purpose, this paper proposed a hybrid Convolutional Neural Network (CNN) – Bidirectional Long Short-Term Memory (BiLSTM) approach to classify ECG signals and evaluates its performance by using raw and preprocessed data, and comparing the results to related studies. Two datasets have been used in this classification. The results were promising, the model has scored 99.00% accuracy on raw data classifying 4 classes, and 99.73% accuracy on a larger preprocessed data for 3 classes classification. The proposed model is suitable to serve in our monitoring task.

Keywords:

Electrocardiography, Deep learning, Internet of Things, convolutional neural network, Bidirectional Long Short-Term Memory

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Makhir, A., El Yousfi Alaoui, M. H., Bellarbi, L., & Jilbab, A. (2024). IOT BASED ECG: HYBRID CNN-BILSTM APPROACH FOR MYOCARDIAL INFARCTION CLASSIFICATION. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 76–80. https://doi.org/10.35784/iapgos.6045

IOT BASED ECG: HYBRID CNN-BILSTM APPROACH FOR MYOCARDIAL INFARCTION CLASSIFICATION

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