Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea.

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第一作者： Tanmoy,Paul

第一单位： Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.;Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, School of Medicine, University of Missouri, Columbia, MO 65211, USA.

作者： Tanmoy,Paul ^[1] ; Omiya,Hassan ^[2] ; Christina S,McCrae ^[3] ; Syed Kamrul,Islam ^[2] ; Abu Saleh Mohammad,Mosa ^[1]

作者单位： Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.;Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, School of Medicine, University of Missouri, Columbia, MO 65211, USA. ^[1] Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA. ^[2] School of Nursing, University of South Florida, Tampa, FL 33620, USA. ^[3]

关键词 apnea depth-wise separable convolution energy efficient AI model fusion transfer learning

DOI 10.3390/diagnostics14222505

PMID 39594171

发布时间 2024-11-30

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Diagnostics (Basel, Switzerland)

2024年14卷22期

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Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea.

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Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea.

Diagnostics (Basel, Switzerland)

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