Real-time cuffless continuous blood pressure estimation using deep learning model

Yung Hui Li, Latifa Nabila Harfiya, Kartika Purwandari, Yue Der Lin

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

Original languageEnglish
Article number5606
Pages (from-to)1-19
Number of pages19
JournalSensors (Switzerland)
Volume20
Issue number19
DOIs
StatePublished - 1 Oct 2020

Keywords

  • Bidirectional LSTM
  • Blood pressure (BP)
  • Deep LSTM
  • Electrocardiogram (ECG)
  • Long short-term memory (LSTM)
  • Photoplethysmogram (PPG)
  • Regression

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