Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components

Abdullah Maraş; Serap Aydin

doi:10.1007/978-981-10-4166-2_5

Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components

Abdullah Maraş
, Serap Aydin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from IAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posterio-temporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state.

Original language	English
Title of host publication	CMBEBIH 2017 - Proceedings of the International Conference on Medical and Biological Engineering, 2017
Editors	Almir Badnjevic
Publisher	Springer Verlag
Pages	26-30
Number of pages	5
ISBN (Print)	9789811041655
DOIs	https://doi.org/10.1007/978-981-10-4166-2_5
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Medical and Biological Engineering, CMBEBIH 2017 - Sarajevo, Bosnia and Herzegovina Duration: 16 Mar 2017 → 18 Mar 2017

Publication series

Name	IFMBE Proceedings
Volume	62
ISSN (Print)	1680-0737

Conference

Conference	International Conference on Medical and Biological Engineering, CMBEBIH 2017
Country/Territory	Bosnia and Herzegovina
City	Sarajevo
Period	16/03/17 → 18/03/17

Keywords

Classification emotion
Data mining
Electroencephalography

Access to Document

10.1007/978-981-10-4166-2_5

Cite this

Maraş, A., & Aydin, S. (2017). Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components. In A. Badnjevic (Ed.), CMBEBIH 2017 - Proceedings of the International Conference on Medical and Biological Engineering, 2017 (pp. 26-30). (IFMBE Proceedings; Vol. 62). Springer Verlag. https://doi.org/10.1007/978-981-10-4166-2_5

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title = "Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components",

abstract = "In this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from IAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posterio-temporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state.",

keywords = "Classification emotion, Data mining, Electroencephalography",

author = "Abdullah Mara{\c s} and Serap Aydin",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; International Conference on Medical and Biological Engineering, CMBEBIH 2017 ; Conference date: 16-03-2017 Through 18-03-2017",

year = "2017",

doi = "10.1007/978-981-10-4166-2\_5",

language = "English",

isbn = "9789811041655",

series = "IFMBE Proceedings",

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pages = "26--30",

editor = "Almir Badnjevic",

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address = "Germany",

}

Maraş, A & Aydin, S 2017, Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components. in A Badnjevic (ed.), CMBEBIH 2017 - Proceedings of the International Conference on Medical and Biological Engineering, 2017. IFMBE Proceedings, vol. 62, Springer Verlag, pp. 26-30, International Conference on Medical and Biological Engineering, CMBEBIH 2017, Sarajevo, Bosnia and Herzegovina, 16/03/17. https://doi.org/10.1007/978-981-10-4166-2_5

Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components. / Maraş, Abdullah; Aydin, Serap.
CMBEBIH 2017 - Proceedings of the International Conference on Medical and Biological Engineering, 2017. ed. / Almir Badnjevic. Springer Verlag, 2017. p. 26-30 (IFMBE Proceedings; Vol. 62).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components

AU - Maraş, Abdullah

AU - Aydin, Serap

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017.

PY - 2017

Y1 - 2017

N2 - In this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from IAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posterio-temporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state.

AB - In this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from IAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posterio-temporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state.

KW - Classification emotion

KW - Data mining

KW - Electroencephalography

UR - https://www.scopus.com/pages/publications/85016012858

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DO - 10.1007/978-981-10-4166-2_5

M3 - Conference contribution

AN - SCOPUS:85016012858

SN - 9789811041655

T3 - IFMBE Proceedings

SP - 26

EP - 30

BT - CMBEBIH 2017 - Proceedings of the International Conference on Medical and Biological Engineering, 2017

A2 - Badnjevic, Almir

PB - Springer Verlag

T2 - International Conference on Medical and Biological Engineering, CMBEBIH 2017

Y2 - 16 March 2017 through 18 March 2017

ER -

Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components

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