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Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach.

Dvey-Aharon Z, Fogelson N, Peled A, Intrator N - PLoS ONE (2015)

Bottom Line: Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity.This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives.The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

ABSTRACT
Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

No MeSH data available.


Related in: MedlinePlus

Accuracy of prediction as a function of events obtained from each subject’s initial recording of stimulation events.By using only the first 7 or 8 events from each subject, the prediction accuracy of the methodology is close to optimum.
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pone.0123033.g004: Accuracy of prediction as a function of events obtained from each subject’s initial recording of stimulation events.By using only the first 7 or 8 events from each subject, the prediction accuracy of the methodology is close to optimum.

Mentions: As indicated in the above results, several initial stimuli possessed the most discriminating variance between the two classes of subjects. The resulting optimal learning parameters show that achieving the best separation with the first 7 to 8 events in each subject results in optimal accuracy. This can be clearly seen in Fig 4 below, which plots the percentage of prediction accuracy as a function of events taken from the beginning of the recordings for each subject.


Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach.

Dvey-Aharon Z, Fogelson N, Peled A, Intrator N - PLoS ONE (2015)

Accuracy of prediction as a function of events obtained from each subject’s initial recording of stimulation events.By using only the first 7 or 8 events from each subject, the prediction accuracy of the methodology is close to optimum.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383331&req=5

pone.0123033.g004: Accuracy of prediction as a function of events obtained from each subject’s initial recording of stimulation events.By using only the first 7 or 8 events from each subject, the prediction accuracy of the methodology is close to optimum.
Mentions: As indicated in the above results, several initial stimuli possessed the most discriminating variance between the two classes of subjects. The resulting optimal learning parameters show that achieving the best separation with the first 7 to 8 events in each subject results in optimal accuracy. This can be clearly seen in Fig 4 below, which plots the percentage of prediction accuracy as a function of events taken from the beginning of the recordings for each subject.

Bottom Line: Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity.This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives.The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

ABSTRACT
Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

No MeSH data available.


Related in: MedlinePlus