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This article discusses the use of deep learning in single-lead atrial fibrillation detection approaches. It explains how convolutional recurrent neural networks and convolutional neural networks are used to achieve multi-classification detection of cardiac arrhythmia, including atrial fibrillation. It also discusses the advantages of deep learning over traditional ML methods, and introduces the deep residual network and dense convolution neural network to ensure network depth and avoid corresponding defects or risks caused by increasing network depth.
