The Open Biomedical Engineering Journal

ISSN: 1874-1207 ― Volume 14, 2020

Non-invasive Estimation of Haemoglobin Level Using PCA and Artificial Neural Networks

M. Lakshmi1, *, P. Manimegalai2
1 Department of Electronics & Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
2 Department of Electronics & Instrumentation Engineering, Karunya Institute of Technology & Sciences, Coimbatore, Tamil Nadu, India



Haemoglobin(Hb) measurement is generally performed by the traditional “fingerstick” test i.e., by invasively drawing blood from the body. Although the conventional laboratory measurement is accurate, it has its own limitations such as time delay, inconvenience of the patient, exposure to biohazards and the lack of real-time monitoring in critical situations. Non-invasive Haemoglobin Measurement (SpHb) has gained enormous attention among researches and can provide an earlier diagnosis to polycythemia, anaemia, various cardiovascular diseases, etc. Currently, Photoplethysmograph signal (PPG) is used for measuring oxygen saturation, to monitor the depth of anesthesia, heart rate and respiration monitoring. But through detailed statistical analysis, PPG signal can provide further information about various blood components.

Investigation / Methodology:

In this paper, an approach for non-invasive measurement of Hb using PPG, Principal Component Analysis (PCA) and Neural Network is proposed. A transmissive type PPG sensor is developed which is interfaced with Crowduino for the acquisition of PPG. From the obtained PPG signal, Principal Components (PC) are extracted. SpHb is predicted followed by the extraction of features from the PC. The analysis involves the SpHb prediction using a single PC, double PC and finally all the three PC. The predicted SpHb is evaluated with Hblab in terms of R-value, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error.


An approach for non-invasive measurement of Hb using Principal Components obtained from the PPG signal is discussed. The SpHb value is compared with the Hblab values. Correlation R-value between SpHb and Hblab is 0.77 when three principal components are used. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) between SpHb and Hblab are 0.3, 0.44 and 0.6633 respectively when SpHb is measured with three principal components. It is evident from the result analysis that SpHb shows the promising result when all the three principal components are used. However, one of the limitations of the work is that the population setting chosen for the work does not include paediatric patients, accurately ill patient, pregnant population and surgical patients. With detailed analysis on a wide range of population setting, Hb prediction using PPG is a promising approach for non-invasive measurement.

Keywords: Non-invasive, Haemoglobin, Photoplethysmography (PPG), PCA, Neural Networks, Mean Absolute Error (MAE).

Article Information

Identifiers and Pagination:

Year: 2019
Volume: 13
Issue: Suppl-1, M3
First Page: 114
Last Page: 119
Publisher Id: TOBEJ-13-114
DOI: 10.2174/1874120701913010114

Article History:

Received Date: 19/03/2019
Revision Received Date: 11/07/2019
Acceptance Date: 12/07/2019
Electronic publication date: 17/12/2019
Collection year: 2019

© 2019 Lakshmi et al

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Electronics & Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India; E-mail:

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