Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease

Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neurology International 2024-11, Vol.16 (6), p.1481-1491
Hauptverfasser: Runnova, Anastasiya E, Zhuravlev, Maksim O, Kiselev, Anton R, Parsamyan, Ruzanna R, Simonyan, Margarita A, Drapkina, Oxana M
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1491
container_issue 6
container_start_page 1481
container_title Neurology International
container_volume 16
creator Runnova, Anastasiya E
Zhuravlev, Maksim O
Kiselev, Anton R
Parsamyan, Ruzanna R
Simonyan, Margarita A
Drapkina, Oxana M
description Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.
doi_str_mv 10.3390/neurolint16060110
format Article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11587428</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A821912694</galeid><doaj_id>oai_doaj_org_article_798a9ba950da41e89ea89d59bd69eab4</doaj_id><sourcerecordid>A821912694</sourcerecordid><originalsourceid>FETCH-LOGICAL-c400t-35fe79bb6864baf16edd245ebd18330a22c86a9b0afa4def179b50fe812006973</originalsourceid><addsrcrecordid>eNplkstuEzEUhkcIRKvSB2CDRmIBmym-zMVeoZC2EKlcFiCW1hn7TOIwsYvtBPUF-tx4mhJRsBe2j7__t499iuI5JWecS_LG4Tb40bpEW9ISSsmj4pgR3lSCd93jw1w0R8VpjGuSG-9Yx-jT4ojLRjSklcfF7XfY4YipOseEOqEp5ytwS4yldeUnr9M2OBjLdwHy-mLMSLA6B2Y62Z1NNxP2BZJFl2L5y6ZVFrnqo08-lOc2-mAwxHLhTFZlBZZ-yHz4YV307lWcGISIz4onA4wRT-_Hk-Lb5cXX-Yfq6vP7xXx2VemakFTxZsBO9n0r2rqHgbZoDKsb7A0VnBNgTIsWZE9ggNrgQDPckAEFZSSn2_GTYrH3NR7W6jrYDYQb5cGqu4APSwUhWT2i6qTITiAbYqCmKCSCkKaRvWnztK-z19u91_W236DR-QkCjA9MH-44u1JLv1OUNqKrmcgOr-8dgv-5xZjUxkaN4wgO_TYqTjlrKe3EdNjLf9C1v_uaiaplR6VsJ-psTy0hZ2Dd4PPBOneDG6u9w8Hm-EwwKilr5SSge4EOPsaAw-H6lKipzNR_ZZY1L_7O-6D4U1T8N9O-0jw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3149719964</pqid></control><display><type>article</type><title>Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>PubMed Central</source><creator>Runnova, Anastasiya E ; Zhuravlev, Maksim O ; Kiselev, Anton R ; Parsamyan, Ruzanna R ; Simonyan, Margarita A ; Drapkina, Oxana M</creator><creatorcontrib>Runnova, Anastasiya E ; Zhuravlev, Maksim O ; Kiselev, Anton R ; Parsamyan, Ruzanna R ; Simonyan, Margarita A ; Drapkina, Oxana M</creatorcontrib><description>Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.</description><identifier>ISSN: 2035-8385</identifier><identifier>ISSN: 2035-8377</identifier><identifier>EISSN: 2035-8377</identifier><identifier>DOI: 10.3390/neurolint16060110</identifier><identifier>PMID: 39585069</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Age ; Brain ; Brain damage ; Brain research ; Care and treatment ; Constipation ; Deep learning ; Development and progression ; Disease ; Diseases ; EEG markers ; Electric properties ; Electroencephalography ; Emotional disorders ; Handwriting ; Health care ; Magnetic resonance imaging ; Medical screening ; Neurons ; Olfaction disorders ; oscillational patterns ; Parkinson’s disease ; polysomnography ; Russia ; signal processing ; Sleep ; Tomography ; wavelet analysis</subject><ispartof>Neurology International, 2024-11, Vol.16 (6), p.1481-1491</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 by the authors. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c400t-35fe79bb6864baf16edd245ebd18330a22c86a9b0afa4def179b50fe812006973</cites><orcidid>0000-0002-8620-1609 ; 0000-0003-3967-3950 ; 0000-0002-2102-164X ; 0000-0003-0323-2635</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587428/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587428/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39585069$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Runnova, Anastasiya E</creatorcontrib><creatorcontrib>Zhuravlev, Maksim O</creatorcontrib><creatorcontrib>Kiselev, Anton R</creatorcontrib><creatorcontrib>Parsamyan, Ruzanna R</creatorcontrib><creatorcontrib>Simonyan, Margarita A</creatorcontrib><creatorcontrib>Drapkina, Oxana M</creatorcontrib><title>Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease</title><title>Neurology International</title><addtitle>Neurol Int</addtitle><description>Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.</description><subject>Accuracy</subject><subject>Age</subject><subject>Brain</subject><subject>Brain damage</subject><subject>Brain research</subject><subject>Care and treatment</subject><subject>Constipation</subject><subject>Deep learning</subject><subject>Development and progression</subject><subject>Disease</subject><subject>Diseases</subject><subject>EEG markers</subject><subject>Electric properties</subject><subject>Electroencephalography</subject><subject>Emotional disorders</subject><subject>Handwriting</subject><subject>Health care</subject><subject>Magnetic resonance imaging</subject><subject>Medical screening</subject><subject>Neurons</subject><subject>Olfaction disorders</subject><subject>oscillational patterns</subject><subject>Parkinson’s disease</subject><subject>polysomnography</subject><subject>Russia</subject><subject>signal processing</subject><subject>Sleep</subject><subject>Tomography</subject><subject>wavelet analysis</subject><issn>2035-8385</issn><issn>2035-8377</issn><issn>2035-8377</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNplkstuEzEUhkcIRKvSB2CDRmIBmym-zMVeoZC2EKlcFiCW1hn7TOIwsYvtBPUF-tx4mhJRsBe2j7__t499iuI5JWecS_LG4Tb40bpEW9ISSsmj4pgR3lSCd93jw1w0R8VpjGuSG-9Yx-jT4ojLRjSklcfF7XfY4YipOseEOqEp5ytwS4yldeUnr9M2OBjLdwHy-mLMSLA6B2Y62Z1NNxP2BZJFl2L5y6ZVFrnqo08-lOc2-mAwxHLhTFZlBZZ-yHz4YV307lWcGISIz4onA4wRT-_Hk-Lb5cXX-Yfq6vP7xXx2VemakFTxZsBO9n0r2rqHgbZoDKsb7A0VnBNgTIsWZE9ggNrgQDPckAEFZSSn2_GTYrH3NR7W6jrYDYQb5cGqu4APSwUhWT2i6qTITiAbYqCmKCSCkKaRvWnztK-z19u91_W236DR-QkCjA9MH-44u1JLv1OUNqKrmcgOr-8dgv-5xZjUxkaN4wgO_TYqTjlrKe3EdNjLf9C1v_uaiaplR6VsJ-psTy0hZ2Dd4PPBOneDG6u9w8Hm-EwwKilr5SSge4EOPsaAw-H6lKipzNR_ZZY1L_7O-6D4U1T8N9O-0jw</recordid><startdate>20241116</startdate><enddate>20241116</enddate><creator>Runnova, Anastasiya E</creator><creator>Zhuravlev, Maksim O</creator><creator>Kiselev, Anton R</creator><creator>Parsamyan, Ruzanna R</creator><creator>Simonyan, Margarita A</creator><creator>Drapkina, Oxana M</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88G</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2M</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8620-1609</orcidid><orcidid>https://orcid.org/0000-0003-3967-3950</orcidid><orcidid>https://orcid.org/0000-0002-2102-164X</orcidid><orcidid>https://orcid.org/0000-0003-0323-2635</orcidid></search><sort><creationdate>20241116</creationdate><title>Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease</title><author>Runnova, Anastasiya E ; Zhuravlev, Maksim O ; Kiselev, Anton R ; Parsamyan, Ruzanna R ; Simonyan, Margarita A ; Drapkina, Oxana M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-35fe79bb6864baf16edd245ebd18330a22c86a9b0afa4def179b50fe812006973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Age</topic><topic>Brain</topic><topic>Brain damage</topic><topic>Brain research</topic><topic>Care and treatment</topic><topic>Constipation</topic><topic>Deep learning</topic><topic>Development and progression</topic><topic>Disease</topic><topic>Diseases</topic><topic>EEG markers</topic><topic>Electric properties</topic><topic>Electroencephalography</topic><topic>Emotional disorders</topic><topic>Handwriting</topic><topic>Health care</topic><topic>Magnetic resonance imaging</topic><topic>Medical screening</topic><topic>Neurons</topic><topic>Olfaction disorders</topic><topic>oscillational patterns</topic><topic>Parkinson’s disease</topic><topic>polysomnography</topic><topic>Russia</topic><topic>signal processing</topic><topic>Sleep</topic><topic>Tomography</topic><topic>wavelet analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Runnova, Anastasiya E</creatorcontrib><creatorcontrib>Zhuravlev, Maksim O</creatorcontrib><creatorcontrib>Kiselev, Anton R</creatorcontrib><creatorcontrib>Parsamyan, Ruzanna R</creatorcontrib><creatorcontrib>Simonyan, Margarita A</creatorcontrib><creatorcontrib>Drapkina, Oxana M</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Psychology Database (Alumni)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>ProQuest Psychology</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Neurology International</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Runnova, Anastasiya E</au><au>Zhuravlev, Maksim O</au><au>Kiselev, Anton R</au><au>Parsamyan, Ruzanna R</au><au>Simonyan, Margarita A</au><au>Drapkina, Oxana M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease</atitle><jtitle>Neurology International</jtitle><addtitle>Neurol Int</addtitle><date>2024-11-16</date><risdate>2024</risdate><volume>16</volume><issue>6</issue><spage>1481</spage><epage>1491</epage><pages>1481-1491</pages><issn>2035-8385</issn><issn>2035-8377</issn><eissn>2035-8377</eissn><abstract>Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>39585069</pmid><doi>10.3390/neurolint16060110</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-8620-1609</orcidid><orcidid>https://orcid.org/0000-0003-3967-3950</orcidid><orcidid>https://orcid.org/0000-0002-2102-164X</orcidid><orcidid>https://orcid.org/0000-0003-0323-2635</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2035-8385
ispartof Neurology International, 2024-11, Vol.16 (6), p.1481-1491
issn 2035-8385
2035-8377
2035-8377
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11587428
source DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; PubMed Central
subjects Accuracy
Age
Brain
Brain damage
Brain research
Care and treatment
Constipation
Deep learning
Development and progression
Disease
Diseases
EEG markers
Electric properties
Electroencephalography
Emotional disorders
Handwriting
Health care
Magnetic resonance imaging
Medical screening
Neurons
Olfaction disorders
oscillational patterns
Parkinson’s disease
polysomnography
Russia
signal processing
Sleep
Tomography
wavelet analysis
title Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T22%3A35%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wavelet-Detected%20Changes%20in%20Nocturnal%20Brain%20Electrical%20Activity%20in%20Patients%20with%20Non-Motor%20Disorders%20Indicative%20of%20Parkinson's%20Disease&rft.jtitle=Neurology%20International&rft.au=Runnova,%20Anastasiya%20E&rft.date=2024-11-16&rft.volume=16&rft.issue=6&rft.spage=1481&rft.epage=1491&rft.pages=1481-1491&rft.issn=2035-8385&rft.eissn=2035-8377&rft_id=info:doi/10.3390/neurolint16060110&rft_dat=%3Cgale_doaj_%3EA821912694%3C/gale_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3149719964&rft_id=info:pmid/39585069&rft_galeid=A821912694&rft_doaj_id=oai_doaj_org_article_798a9ba950da41e89ea89d59bd69eab4&rfr_iscdi=true