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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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Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment
TL;DR: The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.
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A Method for Removal of Low Frequency Components Associated with Head Movements from Dual-Axis Swallowing Accelerometry Signals
TL;DR: Any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy, as shown by the scaling analysis.
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Motor sequence learning-induced neural efficiency in functional brain connectivity.
Helmet T. Karim,Theodore J. Huppert,Kirk I. Erickson,Mariegold E. Wollam,Patrick J. Sparto,Ervin Sejdic,Jessie M. VanSwearingen +6 more
TL;DR: Compared to the control condition, it was found the task‐related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions, and motor learningwas associated with changes in network activity, spatial extent, and connectivity.
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Neck sensor-supported hyoid bone movement tracking during swallowing
TL;DR: The ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck and deep neural networks were used to mathematically describe the relationship between hyoids bone movement and sensor signals.
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Rationale, development, and implementation of the Electrocardiographic Methods for the Prehospital Identification of Non-ST Elevation Myocardial Infarction Events (EMPIRE).
Salah S. Al-Zaiti,Christian Martin-Gill,Ervin Sejdic,Mohammad Alrawashdeh,Clifton W. Callaway +4 more
TL;DR: Concrete ECG algorithms that can quantify NSTE ischemia and allow differential treatment based on such ECG changes could have an immediate clinical impact on patient outcomes.