Key result
Novel real-time ECG system using Random Forest networks achieves ~99% accuracy for detecting cardiac disease.
Why the study?
Current ECG monitoring equipments are bulky and costly, and existing portable devices do not indicate the probable condition of the heart.
Does a real-time Arduino-based ECG system using a Random Forest Neural Network accurately detect cardiac disease?
Does a real-time Arduino-based ECG system using a Random Forest Neural Network accurately detect cardiac disease?
Answers are grounded in this paper and cite their sources.
A novel portable Arduino-based ECG system using a Random Forest Neural Network achieved 99.43% accuracy in detecting cardiac conditions.
For Detection and Treatment of cardiovascular illness electrocardiograms (ECGs) are used which records the real time electrical activity of the heart through leads. The current ECG Monitoring equipments are bulky and costly. Although some portable ECG devices are available, no devices give the probable condition of heart. In this paper a novel heart condition using ECG signal is proposed where the real time ECG signal is acquired through AD8232 sensor and classified using Random Forest Neural Network [1]. The implementation shows 99.43% accuracy.
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Sarkar et al. (2023) studied Cardiovascular illness. Real-time ECG signal acquisition and classification using AD8232 sensor and Random Forest Neural Network was evaluated on Classification accuracy. A novel real-time ECG signal acquisition and classification system using an AD8232 sensor and Random Forest Neural Network achieved 99.43% accuracy for detecting cardiac disease.
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