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.
Papers
More filters
Book ChapterDOI
Hybrid EEG–fTCD Brain–Computer Interfaces
TL;DR: Two novel hybrid brain–computer interfaces based on electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) are introduced and they were shown to balance between speed and accuracy and outperform the existing hybrid BCIs.
Journal ArticleDOI
Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes
Farah Masood,Hussein A. Abdullah,Nitin Seth,Heather A. Simmons,Kevin Brunner,Ervin Sejdic,Dane R. Schalk,William A. Graham,Amber Hoggatt,Douglas L. Rosene,John B. Sledge,Shanker Nesathurai,Shanker Nesathurai,Shanker Nesathurai +13 more
TL;DR: The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).
Proceedings ArticleDOI
Evaluation of a real-time low-power cardiorespiratory sensor for the IoT
TL;DR: The development and exhaustive evaluation of a new ECGbased cardiorespiratory IoT sensor is developed and it is proved that the sensor is fit for the comfortable medical-grade monitoring of the cardiorespiratory activity in order to provide insights of patients health in a telemedicine context.
Patent
Compressive sampling of physiological signals using time-frequency dictionaries based on modulated discrete prolate spheroidal sequences
Ervin Sejdic,Luis F. Chaparro +1 more
TL;DR: In this paper, a method of sampling and reconstructing an original physiological signal obtained from a subject includes acquiring a number of samples of the original signal, and generating a reconstructed physiological signal using the samples and a time-frequency dictionary, the dictionary having bases which are modulated discrete prolate spheroidal sequences.
Proceedings ArticleDOI
Building IoT-enabled wearable medical devices: An application to a wearable, multiparametric, cardiorespiratory sensor
TL;DR: Wearable healthcare will greatly improve patients' quality oflife by using IoT-based wearable devices similar to the sensor developed in this paper, which is a biomedical-grade heart rate, instantaneous heart-rate variability and respiratory sensor.