Cuffless and continuous blood pressure monitoring using photoplethysmography and deep learning

Hypertension, as an important risk factor for cardiovascular diseases, threatens the lives of adults worldwide every year. Therefore, continuous blood pressure (BP) monitoring is necessary for the prevention and early diagnosis of hypertension. To achieve cuffless BP monitoring, an end-to-end BP waveforms prediction model was implemented using photoplethysmography (PPG) and deep learning in this paper. The proposed model successfully predicted three blood pressure values: systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) from PPG waveforms using U-Net, and their mean absolute errors (MAE) and standard deviations (STD) were in the range of 4.54 ±7.42 mmHg, 2.47 ±4.62 mmHg, 1.67 ±3.65 mmHg, respectively, compared to the reference BP values. This provides a possibility to realize non-invasive continuous blood pressure monitoring in daily life through portable wearable devices.