KEYWORDS: Convolution, Electrocardiography, Data modeling, Performance modeling, Education and training, Cardiovascular disorders, Signal processing, Feature extraction, Signal detection, Heart
In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. The model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. The experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. This innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases.
Cardiovascular disease, especially coronary artery disease, is always a threat to human health. Myocardial infarction is a form of coronary artery disease. Cardiologists frequently use electrocardiogram (ECG) to diagnose this condition and ensure the health of their patients. Therefore, studying ECG signal classification can aid doctors in accurately identifying the disease and providing appropriate treatment. We develop structure to extract feature from ECG signals, which achieves excellent performance in classification tasks. A single lead ECG signal typically consists of P, QRS, T, and U waves, which collectively form an ECG signal beat. We utilize R-peak detection technology to obtain ECG signal beats, and extract beat features using a residual network. To avoid the loss of global information, we employ a simple onedimensional convolutional neural network (CNN) to obtain global signal features. The fully connected layer is then used to fuse the features obtained from both beats and global signal features. The classification task is completed based on the fused features. Our designed structure improves performance metrics by at least 2% when compared to the performance of a one-dimensional convolutional neural network and a residual network individually. Additionally, we also introduce the SE block into the residual network, which provides an attention mechanism to effectively suppress unnecessary features and enhance important ones. By comparing the performance of our structure with and without SE block, we prove that SE block can enhance our structure's ability to extract ECG signal characteristics.
KEYWORDS: Denoising, Signal to noise ratio, Data modeling, Convolution, Deep learning, Fourier transforms, Wavelet transforms, Image processing, Education and training, Data analysis
The collected seismic data are highly contaminated due to random noise such as ambient noise, high and low frequency noise and instrument self-noise, which in turn affects the subsequent processing process. With the success of deep learning in image noise reduction, the application of deep learning to noise reduction of seismic data has also obtained better results than traditional noise processing methods. In this paper, we propose a noise reduction method for seismic data based on subspace projection and fast Fourier convolution (FFC). The method uses fast Fourier convolution to divide the channel into two branches, local and global, after downsampling, to update the feature map from two directions, and generates subspace vectors to separate seismic signal and noise according to the seismic data features to construct a seismic data noise reduction network. Experimental results show that the proposed method outperforms traditional seismic data noise reduction methods, such as wavelet transform, Fourier transform noise reduction, and classical deep learning methods FFDNet and NBNet, in terms of signal-to-noise ratio (SNR) measures.
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