Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map

•The time-domain pulse wave is transformed into a feature map of 36-dimensional frequency-domain mel-frequency cepstral coefficients (MFCC), and a pre-training network based on small-sample specific targets is constructed to enhance feature learning capability of pulse wave.•The fusion attention mechanism is added to the inverted residual to improve the global feature correlation. 3 × 3 convolutional and BN layers are added to reduce overfitting.•We study the correlations between the temporal and frequency domain characteristics of pulse wave and hypertensive classification of hypertensive target organ damage(TOD), and analyze the key features of pulse wave affecting TOD in hypertension, so as to provide effective reference for clinical diagnosis of hypertension. The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.