A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

2021 ◽  
Author(s):  
Vivien Gaillet ◽  
Eleonora Borda ◽  
Elodie Geneviève Zollinger ◽  
Diego Ghezzi
Keyword(s):  
Machine Learning ◽  
Electrical Stimulation ◽  
Optic Nerve ◽  
Evoked Potentials ◽  
Visual Stimulation ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Cortical Evoked Potentials ◽  
Stimulation Of

Modulation of cortical evoked potentials by stimulation of nucleus raphe magnus in rats

1992 ◽  
Vol 67 (4) ◽  
pp. 820-828 ◽  
Author(s):  
K. A. Follett ◽  
G. F. Gebhart
Keyword(s):  
Electrical Stimulation ◽  
Evoked Potentials ◽  
Visual Stimulation ◽  
Medial Lemniscus ◽  
Nucleus Raphe Magnus ◽  
Cortical Evoked Potentials ◽  
Test Stimulation ◽  
Raphe Magnus ◽  
Sensory Activity ◽  
Stimulation Of

1. In pentobarbital sodium-anesthetized rats, we evaluated changes in cortical evoked potentials (EPs) associated with electrical and chemical stimulation of nucleus raphe magnus (NRM). A condition-test (C-T) paradigm was used. Cortical EPs were produced by test stimuli delivered to a hindpaw or the thalamic ventral posterior lateral nucleus (VPL; electrical stimulation), or by photic stimulation of the eyes or electrical stimulation of contralateral homotopical cortex (transcallosal EPs). These test stimuli were then preceded by electrical or chemical conditioning stimulation (CS) delivered to NRM through a stereotaxically implanted electrode or injection cannula, respectively. Effects of CS on EPs produced by the test stimuli were characterized. 2. Electrical CS preceding a test stimulus delivered to the foot reduced the amplitude of EPs at thresholds as low as 10-25 microA. The magnitude of EP reduction was dependent on CS intensity, frequency, and the C-T interval. Optimal parameters were trains of 10 pulses (400 Hz) delivered at a C-T interval of 5-10 ms. Injection of glutamate and lidocaine into NRM demonstrated that these effects were due to activation of NRM neurons and not to current spread to medial lemniscus (ML). NRM CS also reduced cortical EPs produced by test stimulation in VPL but did not alter EPs from visual stimulation or from electrical stimulation of contralateral homotopical cortex. 3. These findings suggest that NRM CS attenuates EPs by inhibiting thalamic or thalamocortical afferent activity. Because NRM CS affected all components of the cortical EPs, the effect appears to involve alteration of general sensory activity and is not nociception specific.(ABSTRACT TRUNCATED AT 250 WORDS)

Measurement of Evoked Potentials after Electrical Stimulation of the Human Optic Nerve

2010 ◽  
Vol 51 (10) ◽  
pp. 5351 ◽  
Author(s):  
Marten E. Brelén ◽  
Valerie Vince ◽  
Benoit Gérard ◽  
Claude Veraart ◽  
Jean Delbeke
Keyword(s):  
Electrical Stimulation ◽  
Optic Nerve ◽  
Evoked Potentials ◽  
Human Optic Nerve ◽  
Stimulation Of

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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