Abstract

Photoplethysmography signals are more responsive to changes in blood volume, not vascular pressure. Nowadays, more and more research is being developed for medical purposes, one of which is to diagnose diseases through fingertip pulse waves. This study proposes a new approach to optimize the statistical parameters of regression produced by PPG signals. The fingertip pulse wave device samples the PPG signal in humans and obtains the value of the signal. By taking the following samples, through processing using machine learning to process PPG signal data. machine learning is built to process PPG signal parameter data by the proposed method. The machine learning of feature selection algorithm that used are Forward Feature Selection Algorithm (FFS) and Sequential Input Selection Algorithm (SISAL).

Original languageEnglish
Title of host publication5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages315-320
Number of pages6
ISBN (Electronic)9781665456456
DOIs
Publication statusPublished - 2023
Event5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 - Virtual, Online, Indonesia
Duration: 20 Feb 202323 Feb 2023

Publication series

Name5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023

Conference

Conference5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Country/TerritoryIndonesia
CityVirtual, Online
Period20/02/2323/02/23

Keywords

  • Fast Forward Selection
  • Feature Selection
  • PPG
  • Sequential Input Selection Algorithm

Fingerprint

Dive into the research topics of 'Feature Selection of Photoplethysmograph Data in Machine Learning'. Together they form a unique fingerprint.

Cite this