Novel data augmentation employing multivariate gaussian distribution for neural network-based blood pressure estimation

Kwangsub Song, Tae Jun Park, Joon Hyuk Chang

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a novel data augmentation technique employing multivariate Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) estimation, which incorporates the relationship between the features in a multi-dimensional feature vector to describe the correlated real-valued random variables successfully. To verify the proposed algorithm against the conventional algorithm, we compare the results in terms of mean error (ME) with standard deviation and Pearson correlation using 110 subjects contributed to the database (DB) which includes the systolic BP (SBP), diastolic BP (DBP), photoplethysmography (PPG) signal, and electrocardiography (ECG) signal. For each subject, 3 times (or 6 times) measurements are accomplished in which the PPG and ECG signals are recorded for 20 s. And, to compare with the performance of the BP estimation (BPE) using the data augmentation algorithms, we train the BPE model using the two-stage system, called the stacked NN. Since the proposed algorithm can express properly the correlation between the features than the conventional algorithm, the errors turn out lower compared to the conventional algorithm, which shows the superiority of our approach.

Original languageEnglish
Article number3923
JournalApplied Sciences (Switzerland)
Volume11
Issue number9
DOIs
StatePublished - 2021

Keywords

  • Blood pressure
  • Data augmentation
  • Deep learning
  • Multivariate Gaussian distribution

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