Signal Processing, Computer-Assisted
"Signal Processing, Computer-Assisted" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.
|Signal Interpretation, Computer-Assisted
- Signal Interpretation, Computer-Assisted
- Computer-Assisted Signal Interpretation
- Computer-Assisted Signal Interpretations
- Interpretation, Computer-Assisted Signal
- Interpretations, Computer-Assisted Signal
- Signal Interpretation, Computer Assisted
- Signal Interpretations, Computer-Assisted
Below are MeSH descriptors whose meaning is more general than "Signal Processing, Computer-Assisted".
Below are MeSH descriptors whose meaning is more specific than "Signal Processing, Computer-Assisted".
This graph shows the total number of publications written about "Signal Processing, Computer-Assisted" by people in this website by year, and whether "Signal Processing, Computer-Assisted" was a major or minor topic of these publications.
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|Year||Major Topic||Minor Topic||Total|
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Below are the most recent publications written about "Signal Processing, Computer-Assisted" by people in Profiles.
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals. Comput Biol Med. 2023 03; 155:106641.
Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome. J Neurosci Methods. 2022 04 01; 371:109501.
Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage. 2019 11 15; 202:116091.
Real-World Evaluation of the Eko Electronic Teleauscultation System. Pediatr Cardiol. 2019 Jan; 40(1):154-160.
Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls. PLoS One. 2018; 13(7):e0199144.
Tutorial on X-ray photon counting detector characterization. J Xray Sci Technol. 2018; 26(1):1-28.
Preliminary evaluation of a novel non-linear frequency compression scheme for use in children. Int J Audiol. 2017 12; 56(12):976-988.
Local muscle endurance is associated with fatigue-based changes in electromyographic spectral properties, but not with conduction velocity. J Electromyogr Kinesiol. 2015 Jun; 25(3):451-6.
Spectra of infant EEG within the first year of life: A pilot study. Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2015:4753-6.
Detection of EEG spatial-spectral-temporal signatures of errors: a comparative study of ICA-based and channel-based methods. Brain Topogr. 2015 Jan; 28(1):47-61.