Key result
SVM-based IoT ECG monitoring classifies abnormal rhythms with ~99% accuracy.
Why the study?
Real-time monitoring of cardiovascular disease patients from various locations remains a critical challenge for IoT-based healthcare systems.
An IoT-based ECG monitoring system using SVM classification achieved 98.61% accuracy in distinguishing normal from abnormal ECG signals in a test dataset.
Answers are grounded in this paper and cite their sources.
Supports automated ECG alert development; leaves open prospective outcome trials before clinical use.
In the context of e-Health, we offer an Internet of Things (IoT)-based healthcare system implementation scheme that makes use of electrocardiogram (ECG) sensors and convolutional neural networks (CNNs) in this research. The program intends to improve cardiovascular disease (CVD) patients' access to medical care by facilitating better monitoring and prompt intervention. This implementation makes use of patient path estimator, patient table, and alert management schemes within the hospital to facilitate the localization and prompt intervention for the treatment of CVD patients, as real-time monitoring of patients from various locations remains a critical challenge for IoT-based health care systems.
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A et al. (2024) studied Cardiovascular disease. IoT-based ECG monitoring system was evaluated on Classification accuracy.
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