Your browser doesn't support javascript.
loading
Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.
Yang, Chenxi; Ojha, Banish D; Aranoff, Nicole D; Green, Philip; Tavassolian, Negar.
Affiliation
  • Yang C; School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  • Ojha BD; Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
  • Aranoff ND; Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
  • Green P; Yeshiva University, New York, NY, 10032, USA.
  • Tavassolian N; Columbia University Medical Center, New York, NY, 10032, USA.
Sci Rep ; 10(1): 17521, 2020 10 16.
Article in En | MEDLINE | ID: mdl-33067495
Subject(s)
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aortic Valve Stenosis / Signal Processing, Computer-Assisted / Machine Learning / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Aged / Female / Humans / Male / Middle aged Language: En Journal: Sci Rep Year: 2020 Document type: Article Affiliation country:
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aortic Valve Stenosis / Signal Processing, Computer-Assisted / Machine Learning / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Aged / Female / Humans / Male / Middle aged Language: En Journal: Sci Rep Year: 2020 Document type: Article Affiliation country: