Euclidean Distance based Adaptive Sampling Algorithm for Disassociating Transient and Oscillatory Components of Signals Article Swipe
Safwan Mohammed
,
Neeraj J. Gandhi
,
C. Bourelly
,
Ahmed Dallal
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.23.639754
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.23.639754
Neural signals encode information through oscillatory and transient components. The transient component captures rapid, non-rhythmic changes in response to internal or external events, while the oscillatory component reflects rhythmic patterns critical for processing sensation, action, and cognition. Current spectral and time-domain methods often struggle to distinguish the two components, particularly under sharp transitions, leading to interference and spectral leakage. This study introduces a novel adaptive smoothing algorithm that isolates oscillatory and transient components by dynamically up-sampling signal regions with abrupt changes. The approach leverages Euclidean distance-based thresholds to refine sampling and applies customized smoothing techniques, preserving transient details while minimizing interference. Tested on both synthetic and recorded local field potential data, the algorithm outperformed conventional methods in handling steep signal transitions, as demonstrated by lower mean-square error and improved spectral separation. Our findings highlight the algorithm’s potential to enhance neural signal analysis by more accurately separating components, paving the way for more precise characterization of neural dynamics in research and clinical applications.
Related Topics
Concepts
Transient (computer programming)
Smoothing
Algorithm
Euclidean distance
Computer science
Spectral leakage
SIGNAL (programming language)
Sampling (signal processing)
Time domain
Signal processing
Pattern recognition (psychology)
Artificial intelligence
Digital signal processing
Filter (signal processing)
Fast Fourier transform
Computer vision
Operating system
Programming language
Computer hardware
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.02.23.639754
- https://www.biorxiv.org/content/biorxiv/early/2025/02/27/2025.02.23.639754.full.pdf
- OA Status
- green
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408037675
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408037675Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.02.23.639754Digital Object Identifier
- Title
-
Euclidean Distance based Adaptive Sampling Algorithm for Disassociating Transient and Oscillatory Components of SignalsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-27Full publication date if available
- Authors
-
Safwan Mohammed, Neeraj J. Gandhi, C. Bourelly, Ahmed DallalList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.02.23.639754Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2025/02/27/2025.02.23.639754.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/02/27/2025.02.23.639754.full.pdfDirect OA link when available
- Concepts
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Transient (computer programming), Smoothing, Algorithm, Euclidean distance, Computer science, Spectral leakage, SIGNAL (programming language), Sampling (signal processing), Time domain, Signal processing, Pattern recognition (psychology), Artificial intelligence, Digital signal processing, Filter (signal processing), Fast Fourier transform, Computer vision, Operating system, Programming language, Computer hardwareTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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