Research on Electronic Signal Conversion Based on DTW-K-means Clustering Algorithm and Transformer Model

This paper focuses on analyzing the effectiveness of the electronic signal system for monitoring various vital signs. First, the electronic signal sequences of each detected person are combined, and the combined electronic signal sequences are classified into three classes by using the DTW-K-means clustering algorithm based on LB Keogh acceleration, and their frequency domain characteristics are analyzed by discrete Fourier transform. Then, the electronic signal sequences were translated into vital sign sequences using the Transformer model. Finally, a comparison experiment was conducted, and the results show that the Transformer model is much better than the ordinary time series classification model.