Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach

Abstract Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of thrombosis, vascular injury and infection. Here, we train and validate a computational model for continuous non-invasive ABP estimation in Intensive Care Unit (ICU) patients using a novel wearable sensor array. The sensor acquires continuous high frequency photoplethysmography (PPG) and electrocardiography (ECG) signals which are used as inputs in a deep learning algorithm for beat-to-beat reconstruction of ABP waveforms. We include 28 patients enrolled in four ICU units at Johns Hopkins Hospital, comprising 15,489 five-second ECG and PPG segments. A CNN/LSTM hybrid architecture achieved an R 2 of 0.812 and a sample-level mean absolute error (MAE) of 4.94 ± 4.96 mmHg, with systolic and diastolic blood pressure MAEs of 6.38 ± 6.62 and 3.99 ± 4.53 mmHg, respectively. This performance closely approached an upper-bound model trained on contemporaneously acquired ground truth ECG and PPG signals ( R 2 = 0.824, MAE = 4.81 mmHg), indicating that the sensors retain most hemodynamically relevant information. Split-conformal prediction provided calibrated uncertainty intervals with coverage meeting nominal targets, offering a principled framework for bedside confidence assessment. These findings demonstrate the feasibility of accurate, continuous, non-invasive ABP waveform estimation from wearable biosignals in critically ill patients, establishing a foundation for reducing dependence on invasive arterial monitoring while preserving the waveform-level information essential for hemodynamic management.