Abstract
Cardiac disease, also referred to as cardiovascular disease, is a collection of conditions that affect the heart and blood vessels. Medical professionals typically use a combination of medical history, physical examination, and various diagnostic tests, such as electrocardiograms (ECG/EKG), echocardiograms, and stress tests, to diagnose cardiac diseases. In response to this issue, we are introducing a mobile application that continuously monitors electrocardiogram signals and displays both average and instantaneous heart rates. The aim of this project is to detect and diagnose cardiac diseases so that patients can become informed about their heart health and take appropriate actions based on the results obtained. To identify diseases from real-time ECG data, we used machine learning (ML) classifiers and compared them with offline data to validate the classification. The model we used in our application is pre-trained on the MIT-BIH Arrhythmia Database, which contains a wide range of heart conditions. We used Artificial Neural Network (ANN) as a pre-trained model for multiclass detection as it performed the best among ML models, showing an overall accuracy of 94%. The performance of the model is evaluated by testing it on the application using MIT-BIH test Dataset. On the application, the beat-detecting pre-trained model showed an overall accuracy of 91.178%. The results indicate that the Smart-Health application can accurately classify heart diseases, providing an effective tool for early detection and monitoring of cardiac conditions.
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Acknowledgment
This material is based upon work supported by the National Science Foundation under Grant No. 2105766. The development of the ECG device was performed by Mahfuzur Rahman, Robert Hewitt, and Bashir I. Morshed.
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Utsha, U.T., Hua Tsai, I., Morshed, B.I. (2024). A Smart Health Application for Real-Time Cardiac Disease Detection and Diagnosis Using Machine Learning on ECG Data. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_10
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