Cargando…
Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study
BACKGROUND: With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a...
Autores principales: | Xu, Haoran, Yan, Wei, Lan, Ke, Ma, Chenbin, Wu, Di, Wu, Anshuo, Yang, Zhicheng, Wang, Jiachen, Zang, Yaning, Yan, Muyang, Zhang, Zhengbo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391746/ https://www.ncbi.nlm.nih.gov/pubmed/34387554 http://dx.doi.org/10.2196/25415 |
Ejemplares similares
-
Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment
por: Ma, Chenbin, et al.
Publicado: (2022) -
SenGlove—A Modular Wearable Device to Measure Kinematic Parameters of The Human Hand
por: David, Jonas Paul, et al.
Publicado: (2023) -
Spectral Normalized CycleGAN with Application in Semisupervised Semantic Segmentation of Sonar Images
por: Zhang, Zhisheng, et al.
Publicado: (2022) -
Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals
por: Wang, Jiachen, et al.
Publicado: (2022) -
Sens et non-sens
por: Merleau-Ponty, Maurice, 1908-1961
Publicado: (1948)