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Nigel H. Lovell
Researcher at University of New South Wales
Publications - 678
Citations - 19383
Nigel H. Lovell is an academic researcher from University of New South Wales. The author has contributed to research in topics: Retinal ganglion & Blood pump. The author has an hindex of 58, co-authored 634 publications receiving 16465 citations. Previous affiliations of Nigel H. Lovell include NICTA & AmeriCorps VISTA.
Papers
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Proceedings ArticleDOI
Simulation of parallel current injection for use in a vision prosthesis
TL;DR: In this paper, a 2D computational model was developed to simulate the distribution of voltage arising from multiple parallel current sources connected to a bipolar electrode array and immersed in a bath of physiological saline.
Journal ArticleDOI
Spontaneous fluctuations in the peripheral photoplethysmographic waveform : roles of arterial pressure and muscle sympathetic nerve activity
Gregory S. H. Chan,Azharuddin Fazalbhoy,Ingvars Birznieks,Ingvars Birznieks,Vaughan G. Macefield,Paul M. Middleton,Nigel H. Lovell +6 more
TL;DR: Spontaneous PPG variability in the ear includes a major contribution from arterial pressure and MSNA, which may provide a rationale for its clinical utility and highlight the differential mechanisms governing PPG waveform fluctuations across different body sites.
Proceedings ArticleDOI
Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors
TL;DR: This research will investigate the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs), that will detect falls automatically by recognizing unusual activity sequences in the home environment; hence, decreasing the number of subjects suffering the ‘long-lie’ scenario after a fall.
Journal ArticleDOI
A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device
TL;DR: A fully-coupled cardiac fluid-electromechanics finite element model was developed, incorporating electrical activation, passive and active myocardial mechanics, as well as blood hemodynamics solved simultaneously in an idealized biventricular geometry, able to predict ventricular collapse.
Proceedings ArticleDOI
Characterizing mental load in an arithmetic task using entropy-based features
TL;DR: The entropy-based features of recorded EEG signals are capable of measuring the imposed mental load from the selected channels in two brain regions and may demonstrate that the brain behaves in a more regular or focused manner when dealing with higher task loads.