Limits...
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

Average signal amplitude for healthy subjects and patients diagnosed with schizophrenia within total, early or late sets of events.The first 5 reactions to the stimulations result in peak responses; the variance between healthy subjects and patients diagnosed with schizophrenia is larger than the variance obtained by the average amplitude of 8 entire event sets. Additionally, the last 5 events of both healthy subjects and patients diagnosed with schizophrenia resulted in almost no reaction and almost zero variance between the two classes.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4383331&req=5

pone.0123033.g003: Average signal amplitude for healthy subjects and patients diagnosed with schizophrenia within total, early or late sets of events.The first 5 reactions to the stimulations result in peak responses; the variance between healthy subjects and patients diagnosed with schizophrenia is larger than the variance obtained by the average amplitude of 8 entire event sets. Additionally, the last 5 events of both healthy subjects and patients diagnosed with schizophrenia resulted in almost no reaction and almost zero variance between the two classes.

Mentions: The results showed that the reaction of both diagnosed patients and healthy subjects to non-early stimulations is between weak and non-existent. As seen in Fig 3 below, which is taken from a significantly discriminating electrode obtained from part I, the first several recordings possessed the most variance, which was significantly higher than the variance obtained in the general average. Therefore, better discrimination can be achieved with less data by having a significantly quicker acquisition process.


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)

Average signal amplitude for healthy subjects and patients diagnosed with schizophrenia within total, early or late sets of events.The first 5 reactions to the stimulations result in peak responses; the variance between healthy subjects and patients diagnosed with schizophrenia is larger than the variance obtained by the average amplitude of 8 entire event sets. Additionally, the last 5 events of both healthy subjects and patients diagnosed with schizophrenia resulted in almost no reaction and almost zero variance between the two classes.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123033.g003: Average signal amplitude for healthy subjects and patients diagnosed with schizophrenia within total, early or late sets of events.The first 5 reactions to the stimulations result in peak responses; the variance between healthy subjects and patients diagnosed with schizophrenia is larger than the variance obtained by the average amplitude of 8 entire event sets. Additionally, the last 5 events of both healthy subjects and patients diagnosed with schizophrenia resulted in almost no reaction and almost zero variance between the two classes.
Mentions: The results showed that the reaction of both diagnosed patients and healthy subjects to non-early stimulations is between weak and non-existent. As seen in Fig 3 below, which is taken from a significantly discriminating electrode obtained from part I, the first several recordings possessed the most variance, which was significantly higher than the variance obtained in the general average. Therefore, better discrimination can be achieved with less data by having a significantly quicker acquisition process.

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