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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 2025-3-23 12:48:56 | 只看該作者
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Determination of Vascular Access Stenosis Location and Severity by Multi-domain Analysis of Blood Say was used to transduce bruits, which were filtered and amplified by a transimpedance amplifier. The required bandwidth and dynamic range for the amplifier were determined by analyzing the signal properties of bruits recorded from a vascular phantom. After digital conversion, temporospectral-based
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發(fā)表于 2025-3-24 00:09:38 | 只看該作者
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發(fā)表于 2025-3-24 04:16:21 | 只看該作者
Objective Evaluation Metrics for Automatic Classification of EEG Events,scalar 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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發(fā)表于 2025-3-24 08:11:09 | 只看該作者
Iyad Obeid,Ivan Selesnick,Joseph PiconePresents an interdisciplinary look at research trends in signal processing and biomedicine.Promotes collaboration between healthcare practitioners and signal processing researchers.Includes tutorials
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發(fā)表于 2025-3-24 12:26:53 | 只看該作者
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發(fā)表于 2025-3-24 17:45:03 | 只看該作者
Joost Buurman,Piet Rietveld,Henk Scholtens a noninvasive, low-cost, and easy-to-use neuroimaging tool to measure brain activities in BCIs. An fNIRS-based BCI algorithm receives fNIRS recordings and employs classification techniques to decode the intended tasks. Designing a BCI classification algorithm which can accurately decode user’s int
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發(fā)表于 2025-3-24 19:23:26 | 只看該作者
Joost Buurman,Piet Rietveld,Henk Scholtenon angle variations during locomotion, is to assist diagnosis of knee joint pathologies. These signals are informative but of high dimensionality and high within-subject variability, serious difficulties which are often referred to as the curse of dimensionality. In general, current machine learning
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發(fā)表于 2025-3-24 23:52:28 | 只看該作者
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