Abstract
Abstract To overcome limitations of currently used blood pressure measurement devices in accuracy, continuity and comfort, we propose an approach for blood pressure estimation from electrocardiogram (ECG) signals only. Thereby, statistical signal features are extracted from the ECG which, eventually, serve as input to a random forest regression. The method is trained and tested on MIMIC III waveform data with a large range of blood pressure values. It obtains a mean absolute error ± standard deviation of 3.73 ± 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 ± 7.23 mmHg for systolic blood pressure (SBP), with Pearson coefficients ΥDBP=0.92 and ΥSBP=0.91 respectively.
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