Biomedizinische Technik/Biomedical Engineering This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task
CONCLUSIONS: The proposed method achieved effective classification performance in terms of performance measures.PMID:37935217 | DOI:10.1515/bmt-2023-0407 (Source: Biomedizinische Technik/Biomedical Engineering)
Source: Biomedizinische Technik/Biomedical Engineering - November 7, 2023 Category: Biomedical Engineering Authors: K Venu P Natesan Source Type: research

A new approach towards extracorporeal gas exchange and first < em > in  vitro < /em > results
CONCLUSIONS: This novel class of gas exchangers is characterized by high versatility and expeditious manufacturing. Intraoperability between conventional ECLS systems and dialysis machines broadens the range of application infinitely. Ultimately, long-term clinical applicability ought to be determined over in vivo animal investigations.PMID:37930101 | DOI:10.1515/bmt-2023-0459 (Source: Biomedizinische Technik/Biomedical Engineering)
Source: Biomedizinische Technik/Biomedical Engineering - November 6, 2023 Category: Biomedical Engineering Authors: Foivos Leonidas Mouzakis Ali Kashefi Jan Spillner Stephan R ütten Khosrow Mottaghy Flutura Hima Source Type: research

CT-based evaluation of tissue expansion in cryoablation of ex  vivo kidney
CONCLUSIONS: The mean expansion of ex vivo kidney tissue during cryoablation with a single cryoprobe is 0.31±0.2 mm. The results can be used for identification of basic parameters for optimization of therapy planning.PMID:37924274 | DOI:10.1515/bmt-2023-0174 (Source: Biomedizinische Technik/Biomedical Engineering)
Source: Biomedizinische Technik/Biomedical Engineering - November 4, 2023 Category: Biomedical Engineering Authors: Frank H übner Moritz Klaus Norbert Siedow Christian Leith äuser Thomas Josef Vogl Source Type: research

Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features
Biomed Tech (Berl). 2023 Oct 30. doi: 10.1515/bmt-2023-0332. Online ahead of print.ABSTRACTOBJECTIVES: The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly expe...
Source: Biomedizinische Technik/Biomedical Engineering - October 29, 2023 Category: Biomedical Engineering Authors: Sahbi Chaibi Chahira Mahjoub Wadhah Ayadi Abdennaceur Kachouri Source Type: research