Abstract

Introduction: Accurate, continuous blood pressure (BP) monitoring requires invasive equipment. We investigate the utility of a machine learning algorithm (ML) to estimate the systolic (sys) and diastolic (dia) BP from the electrocardiogram (ECG) and oxygen saturation (SpO2) signals. Methods: We identified over 2,000 hours (~9.2 M heart-beats), of 4 lead ECG, arterial BP, and SpO2 waveforms, from 82 random ICU patients, with various diagnoses. The ECG, BP and SpO2 waveforms were delineated using a wavelet-transform-based algorithm. Features extracted from the ECG and SpO2 signals were used as input to the ML model, while the sys/dia BP values of the corresponding BP signal (range: 0-250 mmHg), served as the gold standard. Using these features, we trained two independent Random Forest (RF) models to estimate the sys/dia BP values, and measured their performance over five-fold cross-validation. To identify the optimal window-length (# heart beats) in estimating sys/dia BP values, the entire process of extracting features and training/testing of RF models was performed for window-lengths of {1,5,6,7,8,9,10,11,12,13,14,15,20,30,50} heart-beats. Results: The root-mean-square error (RMSE) at each window-length is shown in the Figure. The lowest RMSE for both estimated sys/dia BP, 5.81±4.61 mmHg and 3.73±3.26 mmHg, respectively, was achieved at 14 heart-beats. The corresponding RMSE values using a one-beat length-window were 7.73±5.93 mmHg and 5.28±4.42 mmHg, respectively. This result indicates the ability to estimate the instantaneous BP values even on a beat-to-beat basis, with low-error. Conclusions: In comparison to prior studies, our novel ML model, to the best of our knowledge, has been trained over the largest amount of data. Our ML model can accurately estimate the sys/dia values of the arterial BP. Figure: RMSE values obtained while estimating the mean sys/dia BP, across different window-lengths.

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