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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number109823
JournalData in Brief
Volume52
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Dataset
  • Hemoglobin
  • Machine learning
  • Non-invasive
  • Photoplethysmography

Fingerprint

Dive into the research topics of 'A dataset of hemoglobin blood value and photoplethysmography signal for machine learning-based non-invasive hemoglobin measurement'. Together they form a unique fingerprint.

Cite this