Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation

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...

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Vydané v:Applied sciences Ročník 11; číslo 9; s. 3923
Hlavní autori: Song, Kwangsub, Park, Tae-Jun, Chang, Joon-Hyuk
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 2021
Predmet:
Algorithms
Bias
Blood pressure
data augmentation
Deep learning
Electrocardiography
Estimates
multivariate Gaussian distribution
Neural networks
Normal distribution
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11093923