Continuous Blood Pressure Estimation using 1D Convolutional Neural Network and Attention Mechanism

被引：0

作者：

Seo Y. ^{[1
]}

Lee J. ^{[1
]}

Sunarya U. ^{[1
]}

Lee K. ^{[2
]}

Park C. ^{[1
]}

机构：

[1] Department of Computer Engineering, Kwangwoon University, Seoul

[2] R&D Department, TVSTORM Inc., Seoul

来源：

IEIE Transactions on Smart Processing and Computing | 2022年 / 11卷 / 03期

关键词：

1D convolutional neural network; Attention mechanism; Blood pressure; Luong attention;

D O I：

10.5573/IEIESPC.2022.11.3.169

中图分类号：

学科分类号：

摘要：

Patients with hypertensive blood pressure (BP) needs a round-the-clock BP monitoring and must take precautions to prevent emergencies such as stroke or heart failure. This paper suggests a deep neural network (DNNs-based BP estimation approach using electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals. The proposed approach consists of a one-dimensional convolutional neural network (1D CNN) followed by the attention mechanism known as Luong attention. Estimations under the proposed model yield mean absolute error (MAE) of 3.299±2.419 for systolic and 2.69±1.821 for diastolic BP. The algorithm can effectively predict BP without a recurrent neural network (RNNs), which is a typical DNNs model for processing sequential data. Additionally, the proposed approach is preferable owing to its ability to explain the model. © 2022 The Institute of Electronics and Information Engineers.

引用

页码：169 / 173

页数：4

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