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REDUCING NOISE OF INTRACARDIAC ELECTROCARDIOGRAMS USING AN AUTOENCODER AND UTILIZING AND REFINING INTRACARDIAC AND BODY SURFACE ELECTROCARDIOGRAMS USING DEEP LEARNING TRAINING LOSS FUNCTIONS
REDUCING NOISE OF INTRACARDIAC ELECTROCARDIOGRAMS USING AN AUTOENCODER AND UTILIZING AND REFINING INTRACARDIAC AND BODY SURFACE ELECTROCARDIOGRAMS USING DEEP LEARNING TRAINING LOSS FUNCTIONS
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机译:利用深度学习训练损失函数利用自身阳极和利用和精制肠杆儿和体表心电图来降低心电图的噪声
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摘要
A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.
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