Blood pressure estimation using neural networks

被引：13

作者：

Colak, S ^{[1
]}

Isik, C ^{[1
]}

机构：

[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY 13244 USA

来源：

2004 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS | 2004年

关键词：

oscillometry; bloodpressure estimation; neural networks;

D O I：

10.1109/CIMSA.2004.1397222

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Oscillometry is an indirect method to determine blood pressure. An inflatable and deflatable cuff is placed on arm to observe oscillations at different pressure levels. Thus, an envelope obtained from the oscillations is related to the blood pressure. In our work, we extract few features from the oscillometric waveforms, and estimate blood pressure using feedforward neural networks. Feature strength is evaluated by,computing the standard deviation of the errors. The results are compared with the traditional maximum amplitude pressure algorithm. A large non-invasively collected database is used for this purpose.

引用

页码：21 / 25

页数：5

共 50 条

[1] Oscillometry-Based Blood Pressure Estimation Using Convolutional Neural Networks
Choi, Minho
Lee, Sang-Jin
[J]. IEEE ACCESS, 2022, 10 : 56813 - 56822
[2] Oscillometric Blood Pressure Estimation Using Principal Component Analysis and Neural Networks
Forouzanfar, Mohamad
Dajani, Hilmi R.
Groza, Voicu Z.
Bolic, Miodrag
Rajan, Sreeraman
[J]. IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY, 2009, : 981 - 986
[3] Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks
Polinski, Artur
Czuszynski, Krzysztof
Kocejko, Tomasz
[J]. 2018 11TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION (HSI), 2018, : 86 - 92
[4] BLOOD PRESSURE ESTIMATION FROM PPG SIGNALS USING CONVOLUTIONAL NEURAL NETWORKS AND SIAMESE NETWORK
Schlesinger, Oded
Vigderhouse, Nitai
Eytan, Danny
Moshe, Yair
[J]. 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 1135 - 1139
[5] Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks
El Hajj, Chadi
Kyriacou, Panayiotis A.
[J]. 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 4269 - 4272
[6] Foot Plantar Pressure Estimation Using Artificial Neural Networks
Xidias, Elias
Koutkalaki, Zoi
Papagiannis, Panagiotis
Papanikos, Paraskevas
Azariadis, Philip
[J]. PRODUCT LIFECYCLE MANAGEMENT IN THE ERA OF INTERNET OF THINGS, PLM 2015, 2016, 467 : 23 - 32
[7] Non-Invasive Blood Pressure Estimation from Photoplethysmography Signals using Artificial Neural Networks
Bersano, Nicolas
Sanson, Horacio
[J]. 2018 20TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT), 2018, : 29 - 35
[8] A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks
Senturk, Umit
Polat, Kemal
Yucedag, Ibrahim
[J]. APPLIED ACOUSTICS, 2020, 170
[9] Blood Pressure Classification Using the Method of the Modular Neural Networks
Pulido, Martha
Melin, Patricia
Prado-Arechiga, German
[J]. INTERNATIONAL JOURNAL OF HYPERTENSION, 2019, 2019
[10] Prediction of blood pressure variability using deep neural networks
Koshimizu, Hiroshi
Kojima, Ryosuke
Kario, Kazuomi
Okuno, Yasushi
[J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2020, 136

← 1 2 3 4 5 →