Abstract
H-type hypertension increases the risks of stroke and cardiovascular disease, posing a great threat to human health. Pulse diagnosis in traditional Chinese medicine ( TCM ) combined with deep learning can independently predict suspected H-type hypertension patients by analyzing their pulse physiological activities. However, the traditional time-domain feature extraction has a higher noise and baseline drift, affecting the classification accuracy. In this literature, we propose an effective prediction on frequency-domain pulse wave features. First, we filter time-domain pulse waves via removal of high-frequency noises and baseline shift. Second, Hilbert-Huang Transform is explored to transform time-domain pulse wave into frequency-domain waveform characterized by Mel-frequency cepstral coefficients (MFCC). Finally, an improved BiLSTM model, combined with mixed attention mechanism is built to applied for prediction of H-type hypertension. With 337 clinical cases from Longhua Hospital affiliated to Shanghai University of TCM and Hospital of Integrated Traditional Chinese and Western Medicine, the 3-fold cross-validation results show that sensitivity, specificity, accuracy, F1-score and AUC reaches 93.48%, 95.27%, 97.48%, 90.77% and 0.9676, respectively. The proposed model achieves better generalization performance than the classical traditional models. In addition, we calculate the feature importance both in time-domain and frequency-domain according to purity of nodes in Random Forest and study the correlations between features and classification that has a good reference value for TCM clinical auxiliary diagnosis.
The similarities between acceleration pulse wave and Baseline drift data about secondary differential value
