## 1 Introduction  
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. For this, previously reported algorithms for the BP measurement have been extensively studied using polynomial regression, support vector machine, and artificial neural network (NN) [1, 2]. Also, recently, in order to improve the estimation performance, the research using deep learning [3] has been attempted and resulted in superior accuracy.  
However, 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]. To address the fatal weakness, previous researches have been extensively conducted by the deep learning techniques which train a model with the limited label data, such as the siamese network and few-shot learning technique [11, 12, 13, 14, 15, 16, 17]. However, since the deep learning techniques employ the image-based feature, the technique is not proper in our task which uses the signal-based feature. Therefore, to address this problem, an augmentation algorithm is demanded to create a pseudo data for the training DB. For this, previous studies for the BP estimation (BPE) have used a bootstrap algorithm which augments the training DB [3, 18, 19].  
However, the bootstrap algorithm does not work well in the DB of non-diversity and the pseudo data which is created by the bootstrap algorithm does not properly express the characteristics and the correlations between the multi-dimensional features. For this reason, although the training DB is sufficiently augmented, there is significant limitation in performance improvement because the pseudo data has a partially negative impact on DNN training [3]. In addition, previous studies do not consider properly the characteristic of the DB which is collected several times for each subject.  
Thus, in this paper, we review the conventional data augmentation algorithm used to improve the limited performance obtained from the NN model trained with a small DB. And then, in order to overcome the weakness of the conventional algorithm, we propose the novel data augmentation algorithm based on multivariate Gaussian distribution (DAGDGD) for the BPE using the NN. The pseudo data derived from a multivariate Gaussian distribution is much more similar to the characteristics of the real data than when they were generated by the conventional data augmentation method. Specifically, since the relationship between the features of the pseudo data and reference BP is represented by multivariate Gaussian distribution, the pseudo data is constructed more effectively than the conventional data augmentation method. For this reason, since the proposed algorithm boosts the training DB effectively than the previous augmentation algorithm, the NN-based model can estimate the BP better than the original method.  
This paper is composed of as follows. The NN-based BPE employing bootstrap algorithm is described in detail in Section 2. The proposed algorithm is described in detail in Section 3. Next, Section 4 shows the results, and discussion is provided in Section 5. Finally, Section 6 concludes the paper.