Cargando…

EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms...

Descripción completa

Detalles Bibliográficos
Autores principales: Shor, Oded, Yaniv-Rosenfeld, Amit, Valevski, Avi, Weizman, Abraham, Khrennikov, Andrei, Benninger, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840633/
https://www.ncbi.nlm.nih.gov/pubmed/36641536
http://dx.doi.org/10.1038/s41598-023-28009-0
_version_ 1784869677196902400
author Shor, Oded
Yaniv-Rosenfeld, Amit
Valevski, Avi
Weizman, Abraham
Khrennikov, Andrei
Benninger, Felix
author_facet Shor, Oded
Yaniv-Rosenfeld, Amit
Valevski, Avi
Weizman, Abraham
Khrennikov, Andrei
Benninger, Felix
author_sort Shor, Oded
collection PubMed
description The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.
format Online
Article
Text
id pubmed-9840633
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-98406332023-01-16 EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia Shor, Oded Yaniv-Rosenfeld, Amit Valevski, Avi Weizman, Abraham Khrennikov, Andrei Benninger, Felix Sci Rep Article The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders. Nature Publishing Group UK 2023-01-14 /pmc/articles/PMC9840633/ /pubmed/36641536 http://dx.doi.org/10.1038/s41598-023-28009-0 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Shor, Oded
Yaniv-Rosenfeld, Amit
Valevski, Avi
Weizman, Abraham
Khrennikov, Andrei
Benninger, Felix
EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title_full EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title_fullStr EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title_full_unstemmed EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title_short EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
title_sort eeg-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840633/
https://www.ncbi.nlm.nih.gov/pubmed/36641536
http://dx.doi.org/10.1038/s41598-023-28009-0
work_keys_str_mv AT shoroded eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia
AT yanivrosenfeldamit eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia
AT valevskiavi eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia
AT weizmanabraham eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia
AT khrennikovandrei eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia
AT benningerfelix eegbasedspatiotemporalrelationsignaturesforthediagnosisofdepressionandschizophrenia