A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting
This incremental work addresses timely diagnosis and intervention for at-risk patients in critical care settings by improving continuous blood pressure monitoring.
The paper tackled personalized blood pressure forecasting using ECG and PPG signals, achieving accurate and robust predictions across three scenarios with 60 subjects, meeting AAMI standard criteria.
Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings. However, BP varies significantly across individuals, this inter-patient variability motivates the development of personalized models tailored to each patient's physiology. In this work, we propose a personalized BP forecasting model mainly using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. This time-series model incorporates 2D representation learning to capture complex physiological relationships. Experiments are conducted on datasets collected from three diverse scenarios with BP measurements from 60 subjects total. Results demonstrate that the model achieves accurate and robust BP forecasts across scenarios within the Association for the Advancement of Medical Instrumentation (AAMI) standard criteria. This reliable early detection of abnormal fluctuations in BP is crucial for at-risk patients undergoing surgery or intensive care. The proposed model provides a valuable addition for continuous BP tracking to reduce mortality and improve prognosis.