Classification of quantitative eeg data by an artificial neural network: A preliminary study
Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of clas...
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1996
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0302282X_v33_n2_p106_Riquelme http://hdl.handle.net/20.500.12110/paper_0302282X_v33_n2_p106_Riquelme |
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paper:paper_0302282X_v33_n2_p106_Riquelme2023-06-08T15:28:09Z Classification of quantitative eeg data by an artificial neural network: A preliminary study Artificial neural networks Dementia Multiple discriminant function qEEG adult aged algorithm anxiety neurosis article artificial neural network clinical trial controlled clinical trial controlled study dementia depression discriminant analysis electroencephalogram female human major clinical study male mental patient priority journal statistical analysis Adult Aged Anxiety Brain Dementia Depressive Disorder Electroencephalography Female Humans Male Middle Aged Neural Networks (Computer) Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of classical statistical analysis and of neural networks in classifying qEEG data. We were able to obtain a multiple discriminant function using a training set of patients, which classified correctly more than 91% of the qEEGs from an independent group of patients, with less than 5% of false positives. Kohonen’s neural network was trained with the same set of patients. This unsupervised learning artificial neural network performed the classification of the independent sample with an accuracy comparable to that of the multiple discriminant function. Our results suggest that the use of unsupervised learning algorithms could be an interesting alternative in the classification of data obtained from psychiatric patients where definition of their clinical profile is not always a simple task. © 1996 S. Karger AG, Basel. 1996 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0302282X_v33_n2_p106_Riquelme http://hdl.handle.net/20.500.12110/paper_0302282X_v33_n2_p106_Riquelme |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Artificial neural networks Dementia Multiple discriminant function qEEG adult aged algorithm anxiety neurosis article artificial neural network clinical trial controlled clinical trial controlled study dementia depression discriminant analysis electroencephalogram female human major clinical study male mental patient priority journal statistical analysis Adult Aged Anxiety Brain Dementia Depressive Disorder Electroencephalography Female Humans Male Middle Aged Neural Networks (Computer) |
| spellingShingle |
Artificial neural networks Dementia Multiple discriminant function qEEG adult aged algorithm anxiety neurosis article artificial neural network clinical trial controlled clinical trial controlled study dementia depression discriminant analysis electroencephalogram female human major clinical study male mental patient priority journal statistical analysis Adult Aged Anxiety Brain Dementia Depressive Disorder Electroencephalography Female Humans Male Middle Aged Neural Networks (Computer) Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| topic_facet |
Artificial neural networks Dementia Multiple discriminant function qEEG adult aged algorithm anxiety neurosis article artificial neural network clinical trial controlled clinical trial controlled study dementia depression discriminant analysis electroencephalogram female human major clinical study male mental patient priority journal statistical analysis Adult Aged Anxiety Brain Dementia Depressive Disorder Electroencephalography Female Humans Male Middle Aged Neural Networks (Computer) |
| description |
Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of classical statistical analysis and of neural networks in classifying qEEG data. We were able to obtain a multiple discriminant function using a training set of patients, which classified correctly more than 91% of the qEEGs from an independent group of patients, with less than 5% of false positives. Kohonen’s neural network was trained with the same set of patients. This unsupervised learning artificial neural network performed the classification of the independent sample with an accuracy comparable to that of the multiple discriminant function. Our results suggest that the use of unsupervised learning algorithms could be an interesting alternative in the classification of data obtained from psychiatric patients where definition of their clinical profile is not always a simple task. © 1996 S. Karger AG, Basel. |
| title |
Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| title_short |
Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| title_full |
Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| title_fullStr |
Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| title_full_unstemmed |
Classification of quantitative eeg data by an artificial neural network: A preliminary study |
| title_sort |
classification of quantitative eeg data by an artificial neural network: a preliminary study |
| publishDate |
1996 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0302282X_v33_n2_p106_Riquelme http://hdl.handle.net/20.500.12110/paper_0302282X_v33_n2_p106_Riquelme |
| _version_ |
1768543421176217600 |