Apr 1, 2021 · This paper proposes a 1D-CNN-based pulse recognition network structure, which is implemented by the deep learning framework Pytorch.

This paper designs a one-dimensional convolution (1D-CNN) residual neural network structure to identify the pulse signal. First, the original data is sliced and ...

The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification ...

Apr 25, 2022 · To make a complete characterization of the pulse signal for achieving pulse classification recognition, the EPNCC-based method is used in the ...

Pulse Recognition of Cardiovascular Disease Patients Based on One-Dimensional Convolutional Neural Network. Y. Jiao, N. Li, X. Mao, G. Yao, Y. Zhao, and L.

Jan 17, 2024 · The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This ...

Sep 15, 2022 · In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal.

Feb 5, 2024 · Convolutional neural networks (CNN) and particle swarm optimisation (PSO) are utilised in this work to improve the detection and classification ...

Aimed at this problem, a novel method based on convolution neural network (CNN) is presented in this paper. First, ECG signal is preprocessed to suppress the ...

The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification ...