@inproceedings{4c6ce2f7e84546e2bf1c302fa34a56f5,
title = "Augmented reality visualisation for ECG classification and heart disease diagnosis",
abstract = "Augmented Reality (AR) technology is swiftly progressing within the realm of medicine, notably in the domain of biomedical signal processing. It strengthens diagnostic proficiency while fostering interactive 3D virtual environments. The study presents an affordable AR visualisation for Electrocardiography {\textquoteleft}ECG{\textquoteright} arrhythmias classification. The proposal uses two simpli-fied architectures, including Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs). The models achieved reasonable accuracy performance on the PhysioNet MIT-BIH dataset. The advanced AR visualisation capabilities enable medical professionals to make precise diagnoses and formulate effective treatment plans.",
keywords = "ECG, arrhythmias, hear disease, augmented reality, visualisation, deep learning, transfer learning, RNN, LSTM",
author = "Kahina Amara and Guerroudji, \{Mohamed Amine\} and Nadia Zenati and Oussama Kerdjidj and Shadi Atalla and Wathiq Mansoor and Naeem Ramzan",
year = "2024",
month = nov,
day = "26",
doi = "10.1109/RTSI61910.2024.10761433",
language = "English",
isbn = "9798350362145",
series = "IEEE Conference Proceedings",
publisher = "IEEE",
pages = "60--65",
booktitle = "2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)",
address = "United States",
}