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About Predicting Ventricular Fibrillation Through Deep Learning
This document describes a study that developed a deep learning method using a convolutional neural network (CNN) to predict the onset of ventricular fibrillation (VF) using electrocardiography (ECG) data. The CNN model uses short-time Fourier transform (STFT) and continuous wavelet transform (CWT) to extract frequency domain information from the ECG signals. When trained and validated on ECG data from the MIT-BIH database, the proposed 2D STFT/CWT CNN model achieved 99% recall and 97% accuracy in predicting VF onset. This model significantly outperformed 1D and 2D time-domain CNN models, demonstrating the benefit of incorporating frequency domain features for VF prediction. The
Detailed Information
| Author: | ['Oscar Vera'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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