Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Introduction: Schizophrenia affects as many as 24 million people worldwide. Similar to the general population of women, it is estimated that every second woman with schizophrenia becomes a mother. The purpose of the article is to present the difficulties that pregnancy brings for women with schizophrenia in terms of physical and mental health and the course of the disease. Material and methods: The available literature in English and Polish languages was reviewed by searching the PubMed and Google Scholar databases. Articles published from 2009 to 2023 were selected using the following words: schizophrenia, pregnancy, hormones, treatment and outcomes. The analysis encompassed original studies, meta-analyses, randomized controlled trials, and review articles. Results: One of the most significant problems related to motherhood among patients with schizophrenia is the lack of access to knowledge about family planning, sexuality and parenting. Women with schizophrenia are more likely to engage in risky sexual contact, and usually their pregnancies are unplanned. The influence of schizophrenia on the course of pregnancy is still not clear. Studies show that changes in hormone levels during pregnancy, especially estrogen levels, play a protective role in the occurrence of schizophrenic episodes. Nonetheless, pregnant women with schizophrenia have higher risk of miscarriage, infant deaths, obesity, gestational diabetes, hypertension and other obstetric complications. Conclusions: Due to the fact that nowadays more women with schizophrenia may become mothers, it is crucial to provide patients with adequate knowledge about sexual and reproductive life and to ensure them professional, interdisciplinary medical and psychological care during pregnancy. Keywords: schizophrenia, pregnancy, hormones, treatment, outcomes
Introduction: Schizophrenia affects as many as 24 million people worldwide. Similar to the general population of women, it is estimated that every second woman with schizophrenia becomes a mother. The purpose of the article is to present the difficulties that pregnancy brings for women with schizophrenia in terms of physical and mental health and the course of the disease. Material and methods: The available literature in English and Polish languages was reviewed by searching the PubMed and Google Scholar databases. Articles published from 2009 to 2023 were selected using the following words: schizophrenia, pregnancy, hormones, treatment and outcomes. The analysis encompassed original studies, meta-analyses, randomized controlled trials, and review articles. Results: One of the most significant problems related to motherhood among patients with schizophrenia is the lack of access to knowledge about family planning, sexuality and parenting. Women with schizophrenia are more likely to engage in risky sexual contact, and usually their pregnancies are unplanned. The influence of schizophrenia on the course of pregnancy is still not clear. Studies show that changes in hormone levels during pregnancy, especially estrogen levels, play a protective role in the occurrence of schizophrenic episodes. Nonetheless, pregnant women with schizophrenia have higher risk of miscarriage, infant deaths, obesity, gestational diabetes, hypertension and other obstetric complications. Conclusions: Due to the fact that nowadays more women with schizophrenia may become mothers, it is crucial to provide patients with adequate knowledge about sexual and reproductive life and to ensure them professional, interdisciplinary medical and psychological care during pregnancy. Keywords: schizophrenia, pregnancy, hormones, treatment, outcomes
Schizophrenia is a severe mental disorder that impairs a person’s mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.