Testing the Efficacy of Various Artificial Neural Network for Total Haemoglobin Estimation

[1]  SV Subramanian,et al.  Anaemia in low-income and middle-income countries , 2011, The Lancet.

[2]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[3]  P. Rhee,et al.  Non-invasive hemoglobin monitoring. , 2016, International journal of surgery.

[4]  E. McLean,et al.  Worldwide prevalence of anaemia, WHO Vitamin and Mineral Nutrition Information System, 1993–2005 , 2009, Public Health Nutrition.

[5]  Douglas Fisher,et al.  Machine Learning Approaches to Estimating Software Development Effort , 1995, IEEE Trans. Software Eng..

[6]  Harwinder Kaur,et al.  Bayesian Regularization based Neural Network Tool for Software Effort Estimation , 2013 .

[7]  Weidong Wang,et al.  Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration , 2020, J. Electr. Comput. Eng..

[8]  Hongzhi Gao,et al.  Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network. , 2014, Biomedical optics express.

[9]  G. Naik,et al.  Non-invasive hemoglobin measurement using embedded platform , 2020 .

[10]  E. Lewis,et al.  Non-invasive optical real-time measurement of total hemoglobin content , 2010 .

[11]  U. Timm,et al.  Non-invasive continuous online hemoglobin monitoring system , 2010, 2010 IEEE Sensors Applications Symposium (SAS).

[12]  Ling Lin,et al.  Noninvasive hemoglobin measurement using dynamic spectrum. , 2017, The Review of scientific instruments.

[13]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.