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Article

End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism

1
Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea
2
Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Korea
3
Department of Intelligent Information System and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Korea
4
School of Applied Science, Telkom University, Bandung 40257, Indonesia
5
Department of Oriental Biomedical Engineering, Sangji University, Wonju 26339, Korea
6
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
7
Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Korea
8
Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(8), 2338; https://doi.org/10.3390/s20082338
Received: 11 March 2020 / Revised: 13 April 2020 / Accepted: 17 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Biomedical Signal Processing)
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation. View Full-Text
Keywords: blood pressure; electrocardiogram; photoplethysmogram; ballistocardiogram; deep learning; signal processing; attention mechanism blood pressure; electrocardiogram; photoplethysmogram; ballistocardiogram; deep learning; signal processing; attention mechanism
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MDPI and ACS Style

Eom, H.; Lee, D.; Han, S.; Hariyani, Y.S.; Lim, Y.; Sohn, I.; Park, K.; Park, C. End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism. Sensors 2020, 20, 2338. https://doi.org/10.3390/s20082338

AMA Style

Eom H, Lee D, Han S, Hariyani YS, Lim Y, Sohn I, Park K, Park C. End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism. Sensors. 2020; 20(8):2338. https://doi.org/10.3390/s20082338

Chicago/Turabian Style

Eom, Heesang, Dongseok Lee, Seungwoo Han, Yuli S. Hariyani, Yonggyu Lim, Illsoo Sohn, Kwangsuk Park, and Cheolsoo Park. 2020. "End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism" Sensors 20, no. 8: 2338. https://doi.org/10.3390/s20082338

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