Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which...
- Autores:
-
Castillo García, Javier Ferney
Argüello-Prada, Erick Javier
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/16262
- Acceso en línea:
- https://hdl.handle.net/10614/16262
https://red.uao.edu.co/
- Palabra clave:
- Motion artifacts
Photoplethysmogram
Machine learning
Reference signal-less methods
Real-time applications
Computational complexity
Artefactos de movimiento
Fotopletismograma
Aprendizaje automático
Métodos sin señal de referencia
Aplicaciones en tiempo real
Complejidad computacional
- Rights
- openAccess
- License
- Derechos reservados - MDPI, 2025
