A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling

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HighlightsWhat are the main findings?A seven-dimensional data preprocessing technique based on physiological signal priors enhances cross-dataset prediction accuracy. Expanding the raw one-dimensional signal into (amplitude, width, rise/fall time, first/second derivatives, raw signal) dimensions significantly improves waveform prediction performance. For example, on the CHARIS dataset, CBAnet reduced RMSE and MAE by approximately 45% and 50%, respectively, compared to the suboptimal model, while R2 improved by around 39%.CBAnet achieves inter-waveform prediction by unifying local morphology and long-range dependencies. This design achieves the best overall RMSE/MAE/R2 metrics among BiLSTM, CNN-LSTM, Transformer, and Wave-U-Net, with significantly superior peak/phase fidelity and temporal consistency compared to other models. In training on the CHARIS dataset, CBAnet achieved an RMSE of 0.4903 and an R2 of 0.8451, surpassing the performance of other models.What are the implication of the main finding?Advancing real-time non-invasive monitoring. CBAnet’s moderate parameter count and efficient inference enable continuous bedside/edge deployment as a viable solution for intracranial pressure/blood pressure waveform estimation.Cross-dataset generalization capability and clinical utility. Achieved consistent improvements across GBIT-ABP, CHARIS, and PPG-HAF datasets.The real-time, precise monitoring of physiological signals such as intracranial pressure (ICP) and arterial blood pressure (BP) holds significant clinical importance. However, traditional methods like invasive ICP monitoring and invasive arterial blood pressure measurement present challenges including complex procedures, high infection risks, and difficulties in continuous measurement. Consequently, learning-based prediction utilizing observable signals (e.g., BP/pulse waves) has emerged as a crucial alternative approach. Existing models struggle to simultaneously capture multi-scale local features and long-range temporal dependencies, while their computational complexity remains prohibitively high for meeting real-time clinical demands. To address this, this paper proposes a physiological signal prediction method combining composite feature preprocessing with multiscale modeling. First, a seven-dimensional feature matrix is constructed based on physiological prior knowledge to enhance feature discriminative power and mitigate phase mismatch issues. Second, a network architecture CNN-LSTM-Attention (CBAnet), integrating multiscale convolutions, long short-term memory (LSTM), and attention mechanisms is designed to effectively capture both local waveform details and long-range temporal dependencies, thereby improving waveform prediction accuracy and temporal consistency. Experiments on GBIT-ABP, CHARIS, and our self-built PPG-HAF dataset show that CBAnet achieves competitive performance relative to bidirectional long short-term Memory (BiLSTM), convolutional neural network-long short-term memory network (CNN-LSTM), Transformer, and Wave-U-Net baselines across Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). This study provides a promising, efficient approach for non-invasive, continuous physiological parameter prediction.