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Titlebook: Biomedical Signal Processing; Innovation and Appli Iyad Obeid,Ivan Selesnick,Joseph Picone Book 2021 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 16:04:26 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Biomedical Signal Processing
期刊簡稱Innovation and Appli
影響因子2023Iyad Obeid,Ivan Selesnick,Joseph Picone
視頻videohttp://file.papertrans.cn/189/188094/188094.mp4
發(fā)行地址Presents an interdisciplinary look at research trends in signal processing and biomedicine.Promotes collaboration between healthcare practitioners and signal processing researchers.Includes tutorials
圖書封面Titlebook: Biomedical Signal Processing; Innovation and Appli Iyad Obeid,Ivan Selesnick,Joseph Picone Book 2021 The Editor(s) (if applicable) and The
影響因子This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. .Biomedical Signal Processing: Innovation and Applications. presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology.
Pindex Book 2021
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Rural Restructuring in Eastern Coastal Chinascalar figure of merit..In this chapter, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance. We demonstrate these metrics on a seizure detection task based on the TUH EEG Seizure Corpus. We intro
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A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Timehe within-subject variability evaluation for outliers’ removal and reliable curves’ selection. The patterns are then used for deep learning of discriminative classifiers. The proposed pre-processing method encompasses steps of gait events detection, normalization, outlier detection, and cycles’ sele
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