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- W4382935417 abstract "The threat of heart disease has increased throughout history due to the difficulty of diagnosing it and the need for experienced doctors. Heart disease can be caused by various of factors such as age, high blood pressure, and diabetes. This paper investigates the arrythmia of Electrocardiogram (ECG) by comparing and evaluating different classifiers to predict the abnormalities of a heartbeat. We have developed a system that predicts heartbeat arrhythmia using a multi-classifier deep learning model. By using MIT-BIH Arrythmia Database, we were able to achieve an accuracy of 97.4%. Compared to other models that we have implemented; this model recorded the highest performance." @default.
- W4382935417 created "2023-07-04" @default.
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- W4382935417 creator A5092384974 @default.
- W4382935417 date "2023-06-07" @default.
- W4382935417 modified "2023-09-25" @default.
- W4382935417 title "Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform" @default.
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- W4382935417 doi "https://doi.org/10.1109/biosmart58455.2023.10162124" @default.
- W4382935417 hasPublicationYear "2023" @default.
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