Literature DB >> 25571301

A wrist-worn biosensor system for assessment of neurological status.

D Cogan, M Baran Pouyan, M Nourani, J Harvey.   

Abstract

EEG based monitoring for the purpose of assessing a patient's neurological status is conspicuous and uncomfortable at best. We are analyzing a set of physiological signals that may be monitored comfortably by a wrist worn device. We have found that these signals and machine based classification allows us to accurately discriminate among four stress states of individuals. Further, we have found a clear change in these signals during the 70 minutes preceding a single convulsive epileptic seizure. Our classification accuracy on all data has been greater than 90% to date.

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Year:  2014        PMID: 25571301     DOI: 10.1109/EMBC.2014.6944933

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction.

Authors:  Mo Han; Özan Ozdenizci; Ye Wang; Toshiaki Koike-Akino; Deniz Erdoğmuş
Journal:  IEEE Signal Process Lett       Date:  2020-08-31       Impact factor: 3.109

2.  Universal Physiological Representation Learning With Soft-Disentangled Rateless Autoencoders.

Authors:  Mo Han; Ozan Ozdenizci; Toshiaki Koike-Akino; Ye Wang; Deniz Erdogmus
Journal:  IEEE J Biomed Health Inform       Date:  2021-08-05       Impact factor: 7.021

  2 in total

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