A dataset of hemoglobin blood value and photoplethysmography signal for machine learning-based non-invasive hemoglobin measurement

Tomy Abuzairi; Ester Vinia; Muhammad Arkana Yudhistira; Mia Rizkinia; Winda Eriska

doi:10.1016/j.dib.2023.109823

A dataset of hemoglobin blood value and photoplethysmography signal for machine learning-based non-invasive hemoglobin measurement

Data Brief. 2023 Nov 18:52:109823. doi: 10.1016/j.dib.2023.109823. eCollection 2024 Feb.

Authors

Tomy Abuzairi¹, Ester Vinia², Muhammad Arkana Yudhistira¹, Mia Rizkinia³, Winda Eriska⁴

Affiliations

¹ Electrical Engineering, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
² Biomedical Engineering, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
³ Computer Engineering, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
⁴ Faculty of Nursing, Universitas Indonesia, Depok 16424, Indonesia.

Abstract

Hemoglobin (Hb), a protein found within red blood cells, is responsible for transporting oxygen and carbon dioxide gasses. A low concentration of Hb indicates the existence of anemia. Traditional invasive Hb examination methods are accurate but have drawbacks, such as pain. A new approach, non-invasive photoplethysmography (PPG), addresses these issues and allows real-time Hb examination. In this article, the dataset consists of PPG signal, gender, age, and Hb value. The PPG signal was measured by a MAX30102 module sensor that emitted two types of light (red and infra-red light) and measured using a photodetector. Total of 68 subjects (56% female and 44% male) within the age of 18-65 years were collected. The total dataset is 816 data from 68 subjects, which each subject provides 12 sets of red and infra-red light signals. The data were collected at Primary Health Center Jatiuwung, Tangerang City, Banten 15,138, Indonesia. Researchers interested in anemia monitoring and those pursuing the development of non-invasive hemoglobin measurement based on machine learning can leverage this dataset.

Keywords: Dataset; Hemoglobin; Machine learning; Non-invasive; Photoplethysmography.