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Classification of EEG based Diseases using Data Mining

Deepika Kundra, Babita Pandey
2014 International Journal of Computer Applications  
In this work, J48 algorithm is deployed for the classification of EEG based diseases such as: dementia, Schizophrenia, ADHD, epilepsy and mood disorder.  ...  Brain is the most important organ of our body and it serve as centre of nervous system. It is a bioelectric generator.  ...  They use the muscular, cognitive, psychological and EEG signal parameter for the diagnosis. This model faces the problem of rule acquisition.  ... 
doi:10.5120/15819-4643 fatcat:blelhmdx4fhfxk6wvzudlokduu

Diagnosis of EEG-based diseases using data mining and case-based reasoning

Babita Pandey, Depika Kundra
2017 International Journal of Intelligent Systems Design and Computing  
In this work, firstly, J48 algorithm is used for reducing the dimension of parameters. After that CBR is implemented for diagnosis of the different EEG-based diseases.  ...  EEG is a medical imaging techniques that helps to measure the abnormality occurs in the electric activity of the human brain.  ...  They use the muscular, cognitive, psychological and EEG signal parameter for the diagnosis.  ... 
doi:10.1504/ijisdc.2017.082851 fatcat:22ntqgmkzzbrpofn5ozvwqhphy

A review on Machine Learning Techniques for Neurological disorders estimation by Analyzing EEG Waves

Vijaykumar Janga, Prof. E Sreenivasa Reddy
2017 International Journal of Trend in Scientific Research and Development  
The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning  ...  With this exploration, we use a well defined EEG dataset to train as well as test out models.  ...  A list of machine learning techniques for diagnosis of five most common psychological health disorders effectively if the symptoms of the patient are provided as input.  ... 
doi:10.31142/ijtsrd7082 fatcat:yf44mwfip5b55cgq3jyn45liba


Shivangi Jain
2017 International Journal of Advanced Research in Computer Science  
A classification over the given symptoms, its action and medical treatment help in diagnosis. Past studies always parts in important role using which further decision can be taken.  ...  Diagnosis over the disease is complex and not fixed. In the previous research on the same concern shows that the past data can be analyzed properly and it can further be useful for the precautions.  ...  High level of medical knowledge is required.This can be a part of recommendation system and analysis system to opt real time accuracy.SVM approach is used for the classification.  ... 
doi:10.26483/ijarcs.v8i8.4653 fatcat:iv3b542agndb5npogi7k4m2ugm

Neural Engineering for Rehabilitation

Han-Jeong Hwang, Do-Won Kim, Janne M. Hahne, Jongsang Son
2017 BioMed Research International  
, robotaided neurorehabilitation, relation between the features of olfactory stimuli and electroencephalography (EEG), and diagnosis of autism spectrum disorder based on EEG.  ...  As a result, the extreme learning machine showed a mean accuracy of about 70% for all pairwise classification, which was better than the SVM's variants.  ...  Djemal et al. proposed a diagnosis method of autism spectrum disorder based on machine-learning approach.  ... 
doi:10.1155/2017/9638098 pmid:28540305 pmcid:PMC5429912 fatcat:deovjxvdbfd33f3njifsizk3q4

Classification of EEG Signals in Depressed Patients

2020 Balkan Journal of Electrical and Computer Engineering  
In recent studies, EEG has been used as a biomarker for the diagnosis of depression. In this study, EEG signals from 30 patients with clinical depressive disorder have been recorded.  ...  Nowadays, various scales are used in the diagnosis of depressive disorder in individuals. These scales are based on the declaration of the individual.  ...  In addition to the individual declaration, in recent studies, EEG signals have been used as a biomarker for the diagnosis of depression [2, 3] .  ... 
doi:10.17694/bajece.631951 fatcat:zrkpgcqcbjcvfj77qdyuerakg4

Towards Decoding of Depersonalisation Disorder Using EEG: A Time Series Analysis Using CDTW

Abbas Salami, Javier Andreu-Perez, Helge Gillmeister
2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Currently, there is no laboratory method to diagnose DPD, and studies have expressed a period of seven to 12 years for the correct diagnosis of DPD.  ...  We reached 85% accuracy (Kappa 0.7) using leave-one-subject-out cross-validation, which confirms the feasibility for discrimination between DPD patients and a control group using EEG signals.  ...  Then we will use that biomarker as a potential feature to perform a classification task, which can help with the diagnosis of the disorder. The rest of the paper is organised as follows.  ... 
doi:10.1109/ssci47803.2020.9308273 fatcat:f5vtpakw5rhojnzf52qotsjzla

An Overview of Various Computing Methods in Psychiatry and Neuropsychiatry

Prem Shanker Yadav
2017 International Journal for Research in Applied Science and Engineering Technology  
This review paper presents an overview of various computing methods for diagnosis of neuropsychiatric diseases.  ...  Related work is concerned with various computing methods and their involvement in medical diagnosis.  ...  This work investigating temporal specific fractal properties in EEG might serve as a useful approach for characterizing neural basis of AD. III.  ... 
doi:10.22214/ijraset.2017.9183 fatcat:jxqjzm4kfndvrjm4srldqlbv3e

Non-epileptic attack disorder (NEAD): a comprehensive review

P. Francis, G.A. Baker
1999 Seizure  
Non-epileptic attack disorder (NEAD) represents a well-recognized clinical problem with a reported incidence among individuals with a diagnosis of intractable epilepsy as high as 36%.  ...  A failure to identify this disorder may lead to certain risks for the patient including polypharmacy, anticonvulsant toxicity, hazardous intervention, social and economic demands and a lack of recognition  ...  Acknowledgement We would like to thank Professor Mark Williams for reading and commenting on this paper.  ... 
doi:10.1053/seiz.1998.0246 pmid:10091850 fatcat:labci4bynrfh3fuyssavlcyqwq

Page 2640 of Psychological Abstracts Vol. 82, Issue 6 [page]

1995 Psychological Abstracts  
A. Watson, D. & Reynolds, S. (U lowa, Dept of Psychology, lowa City) Diagnosis and classification of psychopathology: Challenges to the current system and future directions.  ...  —Reviews the literature on the Diagnostic and Statistical Man- ual of Mental Disorders (DSM) and focuses on the difficulties, problems, and limitations of the categorical approach to diagnos- tic classification  ... 

Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform [article]

Mohammad Reza Yousefi, Saina Golnejad, Melika Mohammad Hosseini
2022 arXiv   pre-print
Quantification of abnormalities in brain signals can indicate brain conditions and pathology so the electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy.  ...  use of perceptron multilayer neural network can provide a higher accuracy coefficient for experts.  ...  Anuragi and Sisodia presented approach-based machine learning methods and wavelet transform for alcohol use disorder.  ... 
arXiv:2204.04488v1 fatcat:vkdleoqqw5d7vbguvlirhyewfu

Nonepileptic seizures treatment workshop summary

W. Curt LaFrance, Kenneth Alper, Debra Babcock, John J. Barry, Selim Benbadis, Rochelle Caplan, John Gates, Margaret Jacobs, Andres Kanner, Roy Martin, Lynn Rundhaugen, Randy Stewart (+1 others)
2006 Epilepsy & Behavior  
Specific subgroup topics that were covered included: pediatric NES; presenting the diagnosis of NES, outcome measures for NES trials; classification of NES subtypes; and pharmacological treatment approaches  ...  Thus, there is a great need for interdisciplinary collaboration to address the issue of approach to treatment.  ...  Acknowledgments The workshop was funded by the National Institute of Neurological Disorders and Stroke, the National Institute of Mental Health, and the American Epilepsy Society.  ... 
doi:10.1016/j.yebeh.2006.02.004 pmid:16540377 pmcid:PMC5065724 fatcat:7fdo6pajtrf7tmda7jyxs6sa2u

Artificial Intelligence – Machine Learning based Mental Health Diagnosis Automation

In this aspect Medical Image Analysis and psychology have become a promising application domain for Machine Learning (ML) which facilitates an intelligent decision support system for diagnosis.  ...  Mental health of human being is more important parameter and any deficit or issue needs faster diagnosis.  ...  engineering laboratory staff of Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.  ... 
doi:10.26782/jmcms.2019.06.00048 fatcat:w555kgiiwrh65ek2gmawieyf2m

2 Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity [chapter]

2020 Simulations in Medicine  
In this study, the state-of-the-art methods for the recognition of emotions based on electroencephalography data will be presented.  ...  People with disorders such as autism, attention deficit/hyperactivity disorder, or depression may experience social marginalization because of problems with these skills.  ...  The optimal approach would be to search through all the possible EEG channels, spectral bands, and time segments for a set of features that maximize the classification score.  ... 
doi:10.1515/9783110667219-002 fatcat:23zmbmlnkfe5hdadjx5sqpeb5i

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review [article]

Sana Yasin, Syed Asad Hussain, Sinem Aslan, Imran Raza, Muhammad Muzammel, Alice Othmani
2021 arXiv   pre-print
Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection.  ...  This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.  ...  In recent years, ANN-based approaches are used in EEG studies for classification and diagnosis of major depressive disorder.  ... 
arXiv:2009.13402v2 fatcat:tn3i5yogofemtne7m2gae27ibq
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