Key result
A neural network approach for arrhythmia detection on ECG signals achieved 99% efficiency, providing a precise method to facilitate automation in cardiology.
Why the study?
Manual interpretation of ECG reports by medicos highlighted the need to automate heart health monitoring and arrhythmia detection using machine learning.
Can a neural network approach accurately detect arrhythmias on ECG signals?
Can a neural network approach accurately detect arrhythmias on ECG signals?
A neural network approach can detect arrhythmias from ECG signals with 99% efficiency, potentially aiding in automated diagnostics.
May support AI-assisted ECG screening in research; leaves open prospective clinical validation before adoption.
The world has been shook by a rigorous pandemic covid-19 additionally it has accentuated a consequentiality on automating the health sectors from manually reading the reports to utilizing machine learning as an implement to getting the results of findings of sundry reports in an automated manner. There are many studies which have proved that the persons suffering from corona virus had optically discerned its effect on heart health. In rigorous cases it lead to cardiac apprehend proving it to be fatal for the patients. ECG (Electro cardiogram) is undertaken on patients to monitor their heart health; the ECG reports are then manually checked by medicos to conclude about heart health of a person. Cardiology is a study of heart and includes a variety of intricate diseases to be studied. This paper presents an efficient way of arrhythmia detection utilizing dataset which would be subsidiary for implementation of machine learning in this disease detection. Neural network has been utilized in the proposed work and is found to be 99% efficient thereby exhibiting a precise and tested method to further facilitate automation in this sector.
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Yadav et al. (2021) studied Arrhythmia. Neural network approach was evaluated on Efficiency of arrhythmia detection. A neural network approach for arrhythmia detection on ECG signals achieved 99% efficiency, providing a precise method to facilitate automation in cardiology.
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