Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram
Background: This work is a part of a new concept. The aim is to study the measurements that can be used as input to an intelligent system for detecting cerebral malaria from an electroencephalogram (EEG). Materials and Methods: The study of brain connectivity through the calculation of the phase lag...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2020-01-01
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| Series: | Current Medicine Research and Practice |
| Subjects: | |
| Online Access: | http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=5;spage=210;epage=214;aulast=Pelagie |
| _version_ | 1811312897282801664 |
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| author | Temgoua Nanfack Pelagie Flore Jean Marie Kuate Fotso Bénite Isaoura Patrice Abiama Ele |
| author_facet | Temgoua Nanfack Pelagie Flore Jean Marie Kuate Fotso Bénite Isaoura Patrice Abiama Ele |
| author_sort | Temgoua Nanfack Pelagie Flore |
| collection | DOAJ |
| description | Background: This work is a part of a new concept. The aim is to study the measurements that can be used as input to an intelligent system for detecting cerebral malaria from an electroencephalogram (EEG).
Materials and Methods: The study of brain connectivity through the calculation of the phase lag index, which is an adjacency matrix, allows evaluating the units such as degree, density and strength on each channel. These units were evaluated on 29 EEG recordings, consisting of twenty people suffering from coma and nine healthy individuals.
Results: Considering analysis of variance with two factors that are frequency band and group (patient and healthy control), the degree and the density are always higher in healthy children compared to sick children. Nevertheless, the strength is always higher in healthy children compared to sick children with the exception of the delta band on which the values are equal and the alpha band on which the strength is higher in sick children by the report to healthy children.
Conclusion: There is a significant difference (P = 0.002) between the strength of sick people compared to healthy people. Such technology could help reduce the death rate from malaria, in general, and cerebral malaria, in particular, especially in sub-Saharan Africa where this rate is very high. |
| first_indexed | 2024-04-13T10:44:40Z |
| format | Article |
| id | doaj.art-640d49eb554840f4b66d3250e23615e9 |
| institution | Directory Open Access Journal |
| issn | 2352-0817 2352-0825 |
| language | English |
| last_indexed | 2024-04-13T10:44:40Z |
| publishDate | 2020-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Current Medicine Research and Practice |
| spelling | doaj.art-640d49eb554840f4b66d3250e23615e92022-12-22T02:49:49ZengWolters Kluwer Medknow PublicationsCurrent Medicine Research and Practice2352-08172352-08252020-01-0110521021410.4103/cmrp.cmrp_17_20Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogramTemgoua Nanfack Pelagie FloreJean Marie Kuate FotsoBénite IsaouraPatrice Abiama EleBackground: This work is a part of a new concept. The aim is to study the measurements that can be used as input to an intelligent system for detecting cerebral malaria from an electroencephalogram (EEG). Materials and Methods: The study of brain connectivity through the calculation of the phase lag index, which is an adjacency matrix, allows evaluating the units such as degree, density and strength on each channel. These units were evaluated on 29 EEG recordings, consisting of twenty people suffering from coma and nine healthy individuals. Results: Considering analysis of variance with two factors that are frequency band and group (patient and healthy control), the degree and the density are always higher in healthy children compared to sick children. Nevertheless, the strength is always higher in healthy children compared to sick children with the exception of the delta band on which the values are equal and the alpha band on which the strength is higher in sick children by the report to healthy children. Conclusion: There is a significant difference (P = 0.002) between the strength of sick people compared to healthy people. Such technology could help reduce the death rate from malaria, in general, and cerebral malaria, in particular, especially in sub-Saharan Africa where this rate is very high.http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=5;spage=210;epage=214;aulast=Pelagieelectroencephalography; plasmodium falciparum; brain malaria; anova; phase lag index (pli) |
| spellingShingle | Temgoua Nanfack Pelagie Flore Jean Marie Kuate Fotso Bénite Isaoura Patrice Abiama Ele Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram Current Medicine Research and Practice electroencephalography; plasmodium falciparum; brain malaria; anova; phase lag index (pli) |
| title | Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| title_full | Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| title_fullStr | Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| title_full_unstemmed | Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| title_short | Intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| title_sort | intelligent system for early diagnosis of paediatric cerebral malaria from an electroencephalogram |
| topic | electroencephalography; plasmodium falciparum; brain malaria; anova; phase lag index (pli) |
| url | http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=5;spage=210;epage=214;aulast=Pelagie |
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