E
Ervin Sejdic
Researcher at University of Pittsburgh
Publications - 276
Citations - 6881
Ervin Sejdic is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Swallowing & Signal processing. The author has an hindex of 36, co-authored 251 publications receiving 5069 citations. Previous affiliations of Ervin Sejdic include Harvard University & University of Western Ontario.
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Time--frequency feature representation using energy concentration: An overview of recent advances
TL;DR: Time-frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications and the expectation is that further research and applications of these algorithms will flourish in the near future.
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Fractional Fourier transform as a signal processing tool: An overview of recent developments
TL;DR: This paper is geared toward signal processing practitioners by emphasizing the practical digital realizations and applications of the FRFT, which is closely related to other mathematical transforms, such as time-frequency and linear canonical transforms.
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Internet of Medical Things: A Review of Recent Contributions Dealing With Cyber-Physical Systems in Medicine
TL;DR: The practical application of the democratization of medical devices for both patients and health-care providers is described and unexplored research directions and potential trends to solve uncharted research problems are identified.
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Association of Dual-Task Gait With Incident Dementia in Mild Cognitive Impairment: Results From the Gait and Brain Study.
Manuel Montero-Odasso,Yanina Sarquis-Adamson,Mark Speechley,Michael Borrie,Vladimir Hachinski,Jennie Wells,Patricia M. Riccio,Marcelo Schapira,Ervin Sejdic,Richard Camicioli,Robert Bartha,William E. McIlroy,Susan W. Muir-Hunter +12 more
TL;DR: Dual-task gait testing is easy to administer and may be used by clinicians to decide further biomarker testing, preventive strategies, and follow-up planning in patients with MCI.
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A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains
TL;DR: The results showed that some of the extracted features were able to differentiate between healthy and clinical populations, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral, and vertical directions within subjects.