Continuous blood pressure estimation using exclusively photopletysmography by lstm-based signal-to-signal translation

Latifa Nabila Harfiya, Ching Chun Chang, Yung Hui Li

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.

Original languageEnglish
Article number2952
JournalSensors (Switzerland)
Issue number9
StatePublished - 1 May 2021


  • Autoencoder
  • Blood pressure estimation
  • Deep learning
  • LSTM
  • Photopletysmography
  • Signal-to-signal translation


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