Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction
Cardiovascular diseases (CVDs) continue to be a leading cause of mortality globally, highlighting the urgent need for timely and accurate diagnosis. Electrocardiography (ECG) is a vital diagnostic tool for detecting and monitoring various heart conditions by analysing the heart’s electric...
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IEEE
2025-01-01
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| author | Kahina Amara Mohamed Amine Guerroudji Oussama Kerdjidj Nadia Zenati Shadi Atalla Naeem Ramzan |
| author_facet | Kahina Amara Mohamed Amine Guerroudji Oussama Kerdjidj Nadia Zenati Shadi Atalla Naeem Ramzan |
| author_sort | Kahina Amara |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) continue to be a leading cause of mortality globally, highlighting the urgent need for timely and accurate diagnosis. Electrocardiography (ECG) is a vital diagnostic tool for detecting and monitoring various heart conditions by analysing the heart’s electrical activity; however, manually identifying ECG features and classifying heartbeats is a complex and time-consuming process that demands significant expertise. To address this challenge, we have developed ArythmiAR, a novel system that integrates Convolutional Neural Networks (CNN) with Augmented Reality (AR) to enable interactive diagnosis with 3D visualisation and real-time engagement. ArythmiAR offers several key innovations: deep learning-based ECG classification for precise arrhythmia detection, 3D heart modelling and assembly for detailed visualisation, an AR interface for deploying CNN models, 3D localisation of heart sub-regions responsible for arrhythmia anomalies, and enhanced 3D visualisation and interaction capabilities. Our study explores various ECG classification techniques, employing data rebalancing strategies to enhance model performance, with a particular focus on Multilayer Perceptron (MLP) and CNN models, which demonstrated highly competitive results on the PhysioNet MIT-BIH Arrhythmia dataset, achieving an accuracy of 99.07% with the MLP model. Remarkably, this work also involves deploying the ECG classification deep learning model within an AR environment, presenting a prototype for augmented rendering that allows users to localise, visualise, and interact with specific heart regions responsible for arrhythmias. This platform empowers medical professionals to make more accurate diagnoses and develop effective treatment strategies, thereby improving overall patient care. |
| format | Article |
| id | doaj-art-89bd9ea21b8a4c7da068d2c3db95cab3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-89bd9ea21b8a4c7da068d2c3db95cab32025-08-20T03:31:14ZengIEEEIEEE Access2169-35362025-01-011310319810321910.1109/ACCESS.2025.357624311021632Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and InteractionKahina Amara0https://orcid.org/0000-0001-6673-0143Mohamed Amine Guerroudji1Oussama Kerdjidj2https://orcid.org/0000-0001-5390-4917Nadia Zenati3Shadi Atalla4https://orcid.org/0000-0003-3017-9243Naeem Ramzan5https://orcid.org/0000-0002-5088-1462Centre of Development of Advanced Techniques, Algiers, AlgeriaCentre of Development of Advanced Techniques, Algiers, AlgeriaCentre of Development of Advanced Techniques, Algiers, AlgeriaCentre of Development of Advanced Techniques, Algiers, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai, United Arab EmiratesSchool of Computing, Engineering and Physical Sciences, University of the West of Scotland, Scotland, Paisley, U.K.Cardiovascular diseases (CVDs) continue to be a leading cause of mortality globally, highlighting the urgent need for timely and accurate diagnosis. Electrocardiography (ECG) is a vital diagnostic tool for detecting and monitoring various heart conditions by analysing the heart’s electrical activity; however, manually identifying ECG features and classifying heartbeats is a complex and time-consuming process that demands significant expertise. To address this challenge, we have developed ArythmiAR, a novel system that integrates Convolutional Neural Networks (CNN) with Augmented Reality (AR) to enable interactive diagnosis with 3D visualisation and real-time engagement. ArythmiAR offers several key innovations: deep learning-based ECG classification for precise arrhythmia detection, 3D heart modelling and assembly for detailed visualisation, an AR interface for deploying CNN models, 3D localisation of heart sub-regions responsible for arrhythmia anomalies, and enhanced 3D visualisation and interaction capabilities. Our study explores various ECG classification techniques, employing data rebalancing strategies to enhance model performance, with a particular focus on Multilayer Perceptron (MLP) and CNN models, which demonstrated highly competitive results on the PhysioNet MIT-BIH Arrhythmia dataset, achieving an accuracy of 99.07% with the MLP model. Remarkably, this work also involves deploying the ECG classification deep learning model within an AR environment, presenting a prototype for augmented rendering that allows users to localise, visualise, and interact with specific heart regions responsible for arrhythmias. This platform empowers medical professionals to make more accurate diagnoses and develop effective treatment strategies, thereby improving overall patient care.https://ieeexplore.ieee.org/document/11021632/Cardiac arrhythmiaclassificationECGdeep learningaugmented realitydeep learning deployment |
| spellingShingle | Kahina Amara Mohamed Amine Guerroudji Oussama Kerdjidj Nadia Zenati Shadi Atalla Naeem Ramzan Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction IEEE Access Cardiac arrhythmia classification ECG deep learning augmented reality deep learning deployment |
| title | Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction |
| title_full | Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction |
| title_fullStr | Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction |
| title_full_unstemmed | Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction |
| title_short | Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction |
| title_sort | enhancing arrhythmia diagnosis through ecg deep learning classification deploying and augmented reality 3d heart visualization and interaction |
| topic | Cardiac arrhythmia classification ECG deep learning augmented reality deep learning deployment |
| url | https://ieeexplore.ieee.org/document/11021632/ |
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