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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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Quantitative classification of pediatric swallowing through accelerometry

TL;DR: The results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.
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Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation

TL;DR: The proposed method achieved more than 90% accuracy and similar values of sensitivity and specificity when compared to human ratings even when tested over swallows from an independent clinical experiment which demonstrates the clinical significance of high resolution cervical auscultation in replacing ionizing radiation-based evaluation of swallowing kinematics.
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High-Resolution Cervical Auscultation Signal Features Reflect Vertical and Horizontal Displacements of the Hyoid Bone During Swallowing

TL;DR: High-resolution cervical auscultation may offer a noninvasive alternative for dysphagia screening and additional diagnostic information and associations between the patients’ characteristics and auscULTations’ signals were observed.
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A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

TL;DR: A thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series concludes that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.
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Understanding the effects of pre-processing on extracted signal features from gait accelerometry signals

TL;DR: The results indicated that while gait accelerometry is a valuable approach for the gait assessment, one has to carefully adopt any pre-processing steps as they alter the observed findings.