ORCID Profile
0000-0001-8360-3605
Current Organisation
Northeastern University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 29-04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.COMPBIOMED.2022.105254
Abstract: Central aortic blood pressure (CABP) is a better predictor for cardiovascular events than brachial blood pressure. However, direct CABP measurement is invasive. The objective of this paper is to develop an ultrasound-based method using in idualized Windkessel (WK) models for non-invasive estimation of CABP. Three WK models (with two-, three- and four-element WK, named, WK2, WK3 and WK4, respectively) were created and the model parameters were in idualized based on aortic flow velocity and diameter waveforms measured by ultrasound (US). Experimental data were acquired in 42 subjects aged 21-67 years. The CABP estimated by WK models was compared with the reference CABP obtained using a commercial system. The results showed that the overall performance of the WK3 and WK4 models was similar, outperforming the WK2 model. The estimated CABP based on WK3/WK4 model showed good agreement with the reference CABP: the absolute errors of systolic blood pressure (SBP), 2.4 ± 2.1/2.4 ± 2.0 mmHg diastolic blood pressure (DBP), 1.4 ± 1.1/1.7 ± 1.5 mmHg mean blood pressure (MBP), 1.3 ± 0.8/1.3 ± 0.8 mmHg pulse pressure (PP), 3.0 ± 2.3/3.2 ± 2.6 mmHg the root mean square error (RMSE) of the waveforms, 2.5 ± 1.0/2.6 ± 1.1 mmHg. Therefore, the proposed method can provide a non-invasive CABP estimation during routine cardiac US examination.
Publisher: Elsevier BV
Date: 03-2023
Publisher: MDPI AG
Date: 06-04-2019
DOI: 10.3390/S19071656
Abstract: Despite that graphene has been extensively used in flexible wearable sensors, it remains an unmet need to fabricate a graphene-based sensor by a simple and low-cost method. Here, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a sensor is fabricated by sealing of a graphene sheet with polyurethane (PU) medical film. Compared with other graphene-based sensors, it greatly simplifies the fabrication process and enables the effective measurement of signals. The resistance of graphene sheet changes linearly with the deformation of the graphene sensor, which lays a solid foundation for the detection of physiological signals. A signal processing circuit is developed to output the physiological signals in the form of electrical signals. The sensor was used to measure finger bending motion signals, respiration signals and pulse wave signals. All the results demonstrate that the graphene sensor fabricated by the simple and low-cost method is a promising platform for physiological signal measurement.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
No related grants have been discovered for Lisheng XU.