FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136193570> ?p ?o ?g. }

• W3136193570 endingPage "143" @default.
• W3136193570 startingPage "125" @default.
• W3136193570 abstract "• Fusing multi-modal physiological data based on time-frequency ridge. • Compressing physiological data directly at the source. • Embedding spatial, temporal, spectral characteristic in just a unified representation. • Transfer learning from image-based architectures to timeseries classification. • Converting physiological timeseries classification into a visual pattern recognition. Physiological signals processing brings challenges including dimensionality (due to the number of channels), heterogeneity (due to the different range of values) and multimodality (due to the different sources). In this regard, the current study intended, first, to use time-frequency ridge mapping in exploring the use of fused information from joint EEG-ECG recordings in tracking the transition between different states of anesthesia. Second, it investigated the effectiveness of pre-trained state-of-the-art deep learning architectures for learning discriminative features in the fused data in order to classify the states during anesthesia. Experimental data from healthy-brain patients undergoing operation (N = 20) were used for this study. Data was recorded from the BrainStatus device with single ECG and 10 EEG channels. The obtained results support the hypothesis that not only can ridge fusion capture temporal-spectral progression patterns across all modalities and channels, but also this simplified interpretation of time-frequency representation accelerates the training process and yet improves significantly the efficiency of deep models. Classification outcomes demonstrates that this fusion could yields a better performance, in terms of 94.14% precision and 0.28 s prediction time, compared to commonly used data-level fusing methods. To conclude, the proposed fusion technique provides the possibility of embedding time-frequency information as well as spatial dependencies over modalities and channels in just a 2D array. This integration technique shows significant benefit in obtaining a more unified and global view of different aspects of physiological data at hand, and yet maintaining the desired performance level in decision making." @default.
• W3136193570 created "2021-03-29" @default.
• W3136193570 creator A5003079226 @default.
• W3136193570 creator A5005250905 @default.
• W3136193570 creator A5066152439 @default.
• W3136193570 creator A5073384818 @default.
• W3136193570 date "2021-09-01" @default.
• W3136193570 modified "2023-10-17" @default.
• W3136193570 title "Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia" @default.
• W3136193570 cites W1655403841 @default.
• W3136193570 cites W1971065470 @default.
• W3136193570 cites W2012188213 @default.
• W3136193570 cites W2034534502 @default.
• W3136193570 cites W2045673840 @default.
• W3136193570 cites W2084769923 @default.
• W3136193570 cites W2085780052 @default.
• W3136193570 cites W2116184737 @default.
• W3136193570 cites W2153230907 @default.
• W3136193570 cites W2212554588 @default.
• W3136193570 cites W2518936540 @default.
• W3136193570 cites W2616875318 @default.
• W3136193570 cites W2620429297 @default.
• W3136193570 cites W2739486151 @default.
• W3136193570 cites W2802450535 @default.
• W3136193570 cites W2893892260 @default.
• W3136193570 cites W2898716605 @default.
• W3136193570 cites W2903486342 @default.
• W3136193570 cites W2909557476 @default.
• W3136193570 cites W2909695454 @default.
• W3136193570 cites W2913246999 @default.
• W3136193570 cites W2940740645 @default.
• W3136193570 cites W2942336547 @default.
• W3136193570 cites W2953193031 @default.
• W3136193570 cites W2957188486 @default.
• W3136193570 cites W2960598733 @default.
• W3136193570 cites W2962879438 @default.
• W3136193570 cites W2966907001 @default.
• W3136193570 cites W2997308669 @default.
• W3136193570 cites W3003417734 @default.
• W3136193570 cites W3004916592 @default.
• W3136193570 cites W3005165365 @default.
• W3136193570 cites W3013928520 @default.
• W3136193570 cites W3014215018 @default.
• W3136193570 cites W3017309755 @default.
• W3136193570 cites W3022062321 @default.
• W3136193570 cites W3046503247 @default.
• W3136193570 cites W3084624816 @default.
• W3136193570 cites W3104101404 @default.
• W3136193570 cites W3107050463 @default.
• W3136193570 cites W4234031798 @default.
• W3136193570 doi "https://doi.org/10.1016/j.inffus.2021.03.001" @default.
• W3136193570 hasPublicationYear "2021" @default.
• W3136193570 type Work @default.
• W3136193570 sameAs 3136193570 @default.
• W3136193570 citedByCount "5" @default.
• W3136193570 countsByYear W31361935702021 @default.
• W3136193570 countsByYear W31361935702022 @default.
• W3136193570 countsByYear W31361935702023 @default.
• W3136193570 crossrefType "journal-article" @default.
• W3136193570 hasAuthorship W3136193570A5003079226 @default.
• W3136193570 hasAuthorship W3136193570A5005250905 @default.
• W3136193570 hasAuthorship W3136193570A5066152439 @default.
• W3136193570 hasAuthorship W3136193570A5073384818 @default.
• W3136193570 hasBestOaLocation W31361935701 @default.
• W3136193570 hasConcept C108583219 @default.
• W3136193570 hasConcept C111030470 @default.
• W3136193570 hasConcept C118552586 @default.
• W3136193570 hasConcept C119857082 @default.
• W3136193570 hasConcept C150899416 @default.
• W3136193570 hasConcept C151406439 @default.
• W3136193570 hasConcept C153180895 @default.
• W3136193570 hasConcept C154945302 @default.
• W3136193570 hasConcept C15744967 @default.
• W3136193570 hasConcept C17744445 @default.
• W3136193570 hasConcept C199539241 @default.
• W3136193570 hasConcept C2776359362 @default.
• W3136193570 hasConcept C28490314 @default.
• W3136193570 hasConcept C41008148 @default.
• W3136193570 hasConcept C41608201 @default.
• W3136193570 hasConcept C522805319 @default.
• W3136193570 hasConcept C59404180 @default.
• W3136193570 hasConcept C94625758 @default.
• W3136193570 hasConcept C97931131 @default.
• W3136193570 hasConceptScore W3136193570C108583219 @default.
• W3136193570 hasConceptScore W3136193570C111030470 @default.
• W3136193570 hasConceptScore W3136193570C118552586 @default.
• W3136193570 hasConceptScore W3136193570C119857082 @default.
• W3136193570 hasConceptScore W3136193570C150899416 @default.
• W3136193570 hasConceptScore W3136193570C151406439 @default.
• W3136193570 hasConceptScore W3136193570C153180895 @default.
• W3136193570 hasConceptScore W3136193570C154945302 @default.
• W3136193570 hasConceptScore W3136193570C15744967 @default.
• W3136193570 hasConceptScore W3136193570C17744445 @default.
• W3136193570 hasConceptScore W3136193570C199539241 @default.
• W3136193570 hasConceptScore W3136193570C2776359362 @default.
• W3136193570 hasConceptScore W3136193570C28490314 @default.
• W3136193570 hasConceptScore W3136193570C41008148 @default.
• W3136193570 hasConceptScore W3136193570C41608201 @default.

• next