scholarly journals Pregnancy risk factors in autism: a pilot study with artificial neural networks

2015 ◽  
Vol 79 (2) ◽  
pp. 339-347 ◽  
Author(s):  
Enzo Grossi ◽  
Federica Veggo ◽  
Antonio Narzisi ◽  
Angelo Compare ◽  
Filippo Muratori
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Pregnancy Risk ◽  
Artificial Neural

scholarly journals Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

2020 ◽  
Vol 10 (16) ◽  
pp. 5405
Author(s):  
Cornelia Herbert ◽  
Michael Munz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Treadmill Walking ◽  
Human Gait ◽  
Brain Potentials ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Artificial Neural ◽  
Technical Solutions

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

scholarly journals 4216 Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study

1995 ◽  
Vol 35 ◽  
pp. S160
Author(s):  
T. Barisani-Asenbauer ◽  
St. Kaminski ◽  
G. Krenn ◽  
M. Budil ◽  
A. Dietrich ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Organ Culture ◽  
Artificial Neural ◽  
Donor Corneas

Detecting and ranking cash flow risk factors via artificial neural networks technique

2016 ◽  
Vol 69 (5) ◽  
pp. 1801-1806 ◽  
Author(s):  
Mohammad Namazi ◽  
Ahmad Shokrolahi ◽  
Mohammad Sadeghzadeh Maharluie
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cash Flow ◽  
Artificial Neural ◽  
Cash Flow Risk
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