Single-Cycle Pulse Waveform Recognition Method Based on SVM

Abstract

The single-cycle pulse waves’ morphological characteristics can reflect rich physiological and pathological information about the human cardiovascular system, playing an important role in continuous blood pressure monitoring and cardiovascular disease risk warning. Pulse signals belong to weak physiological signals, and the acquisition process is easily affected by factors such as adjacent channel interference and motion artifacts, leading to abnormal cycles and affecting subsequent feature extraction. Therefore, based on support vector machines (SVM), a single-cycle pulse waveform recognition algorithm is proposed. Using a multi-channel pulse wave acquisition device to collect radial artery pulse signals from 150 subjects. Preprocess the signal to obtain a single-cycle pulse waveform. A total of 54 frequency domain and time-frequency domain feature parameters were extracted by using Fourier transform and wavelet transform to extract the frequency domain and time-frequency domain features. Use the principal components analysis (PCA) method to reduce parameter dimensions. Using the SVM for model training to achieve the classification of single-cycle effective pulse signals, pseudo noise and interference signals. The results show that the algorithm’s classification accuracy is 97.42%, indicating that the algorithm can effectively identify pulse signals, pseudo noise and interference signals, laying the foundation for modeling and suppressing interference signals in the next step.