Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks

被引：0

作者：

Kalamatianos, Dimitrios ^{[1
]}

Liatsis, Panos ^{[2
]}

Wellstead, Peter E. ^{[1
]}

机构：

[1] Natl Univ Ireland, Hamilton Inst, Maynooth, Kildare, Ireland

[2] City Univ London, Sch Engn & Math Sci, London EC1V 0HB, England

来源：

2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 | 2006年

关键词：

near-infrared spectroscopy; interferometer; neural networks; urea; creatinine; glucose; oxyhemoglobin;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO(2)). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 run. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).

引用

页码：5476 / +

页数：2

共 50 条

[41] Divergence projections for variable selection in multi-layer perceptron networks
Eleuteri, A
Tagliaferri, R
Milan, L
NEURAL NETS, 2003, 2859 : 287 - 295
[42] Classification and verification through the combination of the multi-layer perceptron and auto-association neural networks
Iversen, A
Taylor, NK
Brown, KE
Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, 2005, : 1166 - 1171
[43] Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks
Sadiq, Alishba
Yahya, Norashikin
2020 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS), 2021,
[44] Classification of Fake News Using Multi-Layer Perceptron
Jehad, Reham
Yousif, Suhad A.
FOURTH INTERNATIONAL CONFERENCE OF MATHEMATICAL SCIENCES (ICMS 2020), 2021, 2334
[45] Thermal Conductivity Prediction of Pure Liquids Using Multi-Layer Perceptron Neural Network
Najafi, Alireza
Hamzehie, Mohammad Ehsan
Najibi, Hesam
Soleimani, Mohammad
Van Gerven, Tom
Van der Bruggen, Bart
Mazinani, Saeed
JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER, 2015, 29 (01) : 197 - 202
[46] MLP+H: a hybrid neural architecture formed by the interaction of hopfield and Multi-Layer Perceptron neural networks
Oliveira, CS
Hernandez, ED
COMPUTATIONAL METHODS IN NEURAL MODELING, PT 1, 2003, 2686 : 166 - 173
[47] Estimating of the Dry Unit Weight of Compacted Soils Using General Linear Model and Multi-layer Perceptron Neural Networks
Kolay, Ersin
Baser, Tugce
APPLIED SOFT COMPUTING, 2014, 18 : 223 - 231
[48] Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks
Yang, Fen
Moayedi, Hossein
Mosavi, Amir
SUSTAINABILITY, 2021, 13 (17)
[49] Investigation and modeling of a hybrid activated sludge system for municipal wastewater treatment using multi-layer perceptron neural networks
Mokhtari, Habib A.
Mirbagheri, Sayed Ahmad
DESALINATION AND WATER TREATMENT, 2021, 210 : 123 - 133
[50] Performance, evaluation, and modeling of an integrated petroleum refinery wastewater treatment system using multi-layer perceptron neural networks
Mokhtari, Habib A.
Mirbagheri, Sayed Ahmad
Dehkordi, Nazli Rafei
DESALINATION AND WATER TREATMENT, 2021, 212 : 31 - 42

← 1 2 3 4 5 →