An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction.
Ab-CPE algorithm
Cerebral edema
Electromagnetic induction
Multi-frequency characteristic analysis
Journal
Biomedical engineering online
ISSN: 1475-925X
Titre abrégé: Biomed Eng Online
Pays: England
ID NLM: 101147518
Informations de publication
Date de publication:
03 Aug 2021
03 Aug 2021
Historique:
received:
07
01
2021
accepted:
26
07
2021
entrez:
4
8
2021
pubmed:
5
8
2021
medline:
15
12
2021
Statut:
epublish
Résumé
Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1-100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1-100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification. The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%. The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.
Sections du résumé
BACKGROUND
BACKGROUND
Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1-100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1-100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification.
RESULTS
RESULTS
The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%.
CONCLUSION
CONCLUSIONS
The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.
Identifiants
pubmed: 34344370
doi: 10.1186/s12938-021-00913-4
pii: 10.1186/s12938-021-00913-4
pmc: PMC8335876
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
74Subventions
Organisme : National Natural Science Foundation of China
ID : 51977214
Organisme : National Natural Science Foundation of China
ID : 62001070
Organisme : Natural Science Foundation of Chongqing
ID : cstc2020jcyj-msxmX0322
Organisme : Chongqing Technology Innovation and Application Demonstration Project
ID : cstc2018jscx-msybX1094
Informations de copyright
© 2021. The Author(s).
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