EEG analysis in patients with schizophrenia based on microstate semantic modeling method
IntroductionMicrostate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it ha...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1372985/full |
| _version_ | 1797224355606822912 |
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| author | Hongwei Li Changming Wang Lin Ma Cong Xu Haifeng Li |
| author_facet | Hongwei Li Changming Wang Lin Ma Cong Xu Haifeng Li |
| author_sort | Hongwei Li |
| collection | DOAJ |
| description | IntroductionMicrostate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.MethodsThis study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.ResultsThe SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.DiscussionThis research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method. |
| first_indexed | 2024-04-24T13:51:48Z |
| format | Article |
| id | doaj.art-b03402f349334c5e94e3261c44ec27fd |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-04-24T13:51:48Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-b03402f349334c5e94e3261c44ec27fd2024-04-04T04:41:34ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612024-04-011810.3389/fnhum.2024.13729851372985EEG analysis in patients with schizophrenia based on microstate semantic modeling methodHongwei Li0Changming Wang1Lin Ma2Cong Xu3Haifeng Li4Faculty of Computing, Harbin Institute of Technology, Harbin, ChinaDepartment of Neurosurgery, XuanWu Hospital, Capital Medical University, Beijing, ChinaFaculty of Computing, Harbin Institute of Technology, Harbin, ChinaFaculty of Computing, Harbin Institute of Technology, Harbin, ChinaFaculty of Computing, Harbin Institute of Technology, Harbin, ChinaIntroductionMicrostate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.MethodsThis study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.ResultsThe SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.DiscussionThis research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.https://www.frontiersin.org/articles/10.3389/fnhum.2024.1372985/fullschizophreniamicrostate analysissemantic featuresquality featuresdual-microstate templates |
| spellingShingle | Hongwei Li Changming Wang Lin Ma Cong Xu Haifeng Li EEG analysis in patients with schizophrenia based on microstate semantic modeling method Frontiers in Human Neuroscience schizophrenia microstate analysis semantic features quality features dual-microstate templates |
| title | EEG analysis in patients with schizophrenia based on microstate semantic modeling method |
| title_full | EEG analysis in patients with schizophrenia based on microstate semantic modeling method |
| title_fullStr | EEG analysis in patients with schizophrenia based on microstate semantic modeling method |
| title_full_unstemmed | EEG analysis in patients with schizophrenia based on microstate semantic modeling method |
| title_short | EEG analysis in patients with schizophrenia based on microstate semantic modeling method |
| title_sort | eeg analysis in patients with schizophrenia based on microstate semantic modeling method |
| topic | schizophrenia microstate analysis semantic features quality features dual-microstate templates |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1372985/full |
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