Use of a computer diagnostic system to assess cerebral cortical function in rabbits on the basis of eeg data

1978 ◽  
Vol 86 (2) ◽  
pp. 1115-1118
Author(s):  
V. M. Anan'ev ◽  
I. A. Vinokurova ◽  
Yu. G. Maslov
Keyword(s):  
Diagnostic System ◽  
Cortical Function ◽  
Eeg Data ◽  
Computer Diagnostic System

Distributed computer diagnostic system KardioVid on the base of the multi-agent technologies

2017 ◽  
Author(s):  
Oleg N. Bodin ◽  
Anatoly G. Ubiennykh ◽  
Anton S. Sergeenkov ◽  
Svetlana A. Balakhonova ◽  
Fagim K. Rakhmatullov ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Multi Agent ◽  
Computer Diagnostic System

IMITATION MODELING OF MULTIAGENT TECHNOLOGY IN COMPUTER DIAGNOSTIC SYSTEM «CARDIOVID»

2019 ◽  
Author(s):  
O. N. Bodin ◽  
◽  
Z. I. Bausova ◽  
O. E. Bezborodova ◽  
A. G. Ubiennykh ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Imitation Modeling ◽  
Computer Diagnostic System

An Analysis Research for Digitized Features of Epileptic EEG Using SVM

2012 ◽  
Vol 239-240 ◽  
pp. 1169-1172 ◽  
Author(s):  
Xin Xu ◽  
Bin Lv ◽  
Jie Song ◽  
Wei Xiang Shi ◽  
Yan Ting Hu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Spectral Feature ◽  
Diagnostic System ◽  
Epileptic Seizures ◽  
Daily Lives ◽  
Power Spectral ◽  
Eeg Data ◽  
Interictal Eeg ◽  
Focus Localization ◽  
Visual Screening

Epilepsy is one of the most common neurological disorders that greatly disturb patients’ daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. We proposed to study automated epileptic diagnosis using interictal EEG data that was much easier to collect than ictal data. The research aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. This system could also test epileptic seizures in order to provide doctors with further tests and potential monitor of patients. To test such a system, we extract power spectral feature, Petrosian fractal dimension, Higuchi fractal dimension and Hjorth parameters for analysis, from which we find our system can be used in patient monitoring(seizure detection) and seizure focus localization, with 98.333% and75.5% accuracy respectively.

scholarly journals Predicting various forms of endometriosis using artificial neural networks

2020 ◽  
Vol 35 (4) ◽  
pp. 143-149
Author(s):  
N. G. Sazonova ◽  
T. A. Makarenko ◽  
A. N. Narkevich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Computer System ◽  
Clinical Manifestations ◽  
Diagnostic System ◽  
Artificial Neural ◽  
Computer Diagnostic System

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

scholarly journals GW26-e0388 Adult Common Diseases Computer Diagnostic System

2015 ◽  
Vol 66 (16) ◽  
pp. C275
Author(s):  
Hongyuan Bai ◽  
Yajuan Ni ◽  
Xin Dong ◽  
Xiu Han ◽  
Tingzhong Wang ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Common Diseases ◽  
Computer Diagnostic System
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