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.
Highlights
A blood pressure (BP) is an essential factor to diagnose a health condition, and it is important to periodically monitor the BP for our healthcare
Since an advantage of the deep neural network (DNN) which works well with the large DB is limited with the small DB, the DNN model which is trained by small training DB yields a fatal weakness [4,5,6,7,8,9,10]
In order to overcome the weakness of the conventional algorithm, we propose the novel data augmentation algorithm based on multivariate Gaussian distribution (DAMGD) for the BP estimation (BPE) using the NN
Summary
A blood pressure (BP) is an essential factor to diagnose a health condition, and it is important to periodically monitor the BP for our healthcare. A collection of the biological data such as the BP is mostly limited because the cost is very high for large database (DB) which includes data and its label, verified by expert For this reason, since an advantage of the deep neural network (DNN) which works well with the large DB is limited with the small DB, the DNN model which is trained by small training DB yields a fatal weakness [4,5,6,7,8,9,10]. Since the deep learning techniques employ the image-based feature, the technique is not proper in our task which uses the signal-based feature To address this problem, an augmentation algorithm is demanded to create a pseudo data for the training DB. Previous studies for the BP estimation (BPE) have used a bootstrap algorithm which augments the training DB [3,18,19]
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