Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were rec...
Main Authors: | Donggeun Roh, Hangsik Shin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/6/2188 |
Similar Items
-
A Photoplethysmogram Dataset for Emotional Analysis
by: Ye-Ji Jin, et al.
Published: (2022-06-01) -
Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
by: Gašper Slapničar, et al.
Published: (2019-08-01) -
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms
by: Nabil Ibtehaz, et al.
Published: (2022-11-01) -
Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal
by: Moajjem Hossain Chowdhury, et al.
Published: (2022-10-01) -
Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography
by: Mantas Rinkevičius, et al.
Published: (2023-02-01)