A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection.
IEEE Int Conf Connect Health Appl Syst Eng Technol
; 2021: 149-154, 2021 Dec.
Article
en En
| MEDLINE
| ID: mdl-35253019
ABSTRACT
Recent advances in Critical Congenital Heart Disease (CCHD) research using Photoplethysmography (PPG) signals have yielded an Internet of Things (IoT) based enhanced screening method that performs CCHD detection comparable to SpO2 screening. The use of PPG signals, however, poses a challenge due to its measurements being prone to artifacts. To comprehensively study the most effective way to remove the artifact segments from PPG waveforms, we performed feature engineering and investigated both Machine Learning (ML) and rule based algorithms to identify the optimal method of artifact detection. Our proposed artifact detection system utilizes a 3-stage ML model that incorporates both Gradient Boosting (GB) and Random Forest (RF). The proposed system achieved 84.01% of Intersection over Union (IoU), which is competitive to state-of-the-art artifact detection methods tested on higher resolution PPG.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
IEEE Int Conf Connect Health Appl Syst Eng Technol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos