Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Zhenning Mei"'
Publikováno v:
IEEE Access, Vol 7, Pp 70059-70076 (2019)
Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In m
Externí odkaz:
https://doaj.org/article/9ea330163b764524b604d940213e6fc2
Publikováno v:
IEEE Access, Vol 6, Pp 53566-53575 (2018)
Atrial fibrillation (AF) is one of the most common sustained arrhythmias, affecting about 1% of the population around the world. Rapid popularization of portable and wearable devices in recent years makes widespread personalized and mobile healthcare
Externí odkaz:
https://doaj.org/article/2f396661929e4b4898c08ac05768cfb4
Publikováno v:
Entropy, Vol 21, Iss 11, p 1057 (2019)
To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This era
Externí odkaz:
https://doaj.org/article/2c64f4e797c94a20b95843cb60ee513c
Publikováno v:
Sensors, Vol 18, Iss 6, p 1720 (2018)
Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neur
Externí odkaz:
https://doaj.org/article/4f88df91b2f644499231bbcc7ba18095
Publikováno v:
Sensors, Vol 16, Iss 12, p 2134 (2016)
Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movem
Externí odkaz:
https://doaj.org/article/b167dd84860f41238992c3fe401ff693
Autor:
Wei Yuan, Yongfeng Mei, Gaoshan Huang, Linkai Tao, Zhenning Mei, Zherui Cao, Chen Chen, Yuting Zhao, Zeyu Wang, Wei Chen, Hongyu Chen, Ranran Wang, Wei Li
Publikováno v:
IEEE Sensors Journal. 19:8502-8513
The increased prevalence of chronic disease in aging population entails health risks and imposes significant economic and social burden. It is essential to provide comfortable, cost-effective, and easy-to-use unobtrusive and wearable systems for pers
Publikováno v:
EMBC
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, 3386-3389
STARTPAGE=3386;ENDPAGE=3389;TITLE=40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, 3386-3389
STARTPAGE=3386;ENDPAGE=3389;TITLE=40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4340854ba3115bf7bfb4414d893306f4
https://doi.org/10.1109/EMBC.2018.8512919
https://doi.org/10.1109/EMBC.2018.8512919
Publikováno v:
EMBC
Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) technique. Most previous studies have focused on construct
Publikováno v:
2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018, 2018-January, 173-176
BSN
BSN
A novel dry disposable electrode using carbonized foam as conductive material is presented. The conductive material is flexible and the manufacturing of it is inexpensive. In this paper, the preparation of the conductive material and the electrical p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de71c76db2f78ad772138d08a22c60b5
https://research.tue.nl/en/publications/5733bf29-7d40-4173-bb5c-045748b03dd1
https://research.tue.nl/en/publications/5733bf29-7d40-4173-bb5c-045748b03dd1
Autor:
Hongyu Chen, Chunmei Lu, Zhenning Mei, Laishuan Wang, Wei Chen, Sidarto Bambang Oetomo, Qixin Xu, Ke Xu, Feng Shu, Xiao Gu, Kai Yan
Publikováno v:
BSN
2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 9-12 May 2017, Eindhoven, The Netherlands, 27-30
STARTPAGE=27;ENDPAGE=30;TITLE=2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 9-12 May 2017, Eindhoven, The Netherlands
2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 9-12 May 2017, Eindhoven, The Netherlands, 27-30
STARTPAGE=27;ENDPAGE=30;TITLE=2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 9-12 May 2017, Eindhoven, The Netherlands
—A novel wearable sensor system for seizure monitoring of neonates comprised of smart clothing, video recording and cloud platform is presented. Textile electrodes and Inertial Measurement Unit (IMU) are embedded in the smart clothing to obtain ECG