Szczegóły publikacji
Opis bibliograficzny
A reliable method to estimate the bispectral index value using a single frontal EEG channel for intra and inter subject variability / Mohammad Shahbakhti, Matin Beiramvand, Róża Krycińska, Erfan Nasiri, Wei Chen, Jordi Solé-Casals, Michał Wierzchoń, Anna BRONIEC-WÓJCIK, Piotr AUGUSTYNIAK, Vaidotas Marozas // W: MeMeA 2023 [Dokument elektroniczny] : IEEE international symposium on Medical Measurements and Applications : 14–16.06.2023, Jeju : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2023. — e-ISBN: 978-1-6654-9384-0. — S. [1–5]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [5], Abstr. — Publikacja dostępna online od: 2023-07-10
Autorzy (10)
- Shahbakhti Mohammad
- Beiramvand Matin
- Krycińska Róża
- Nasiri Erfan
- Chen Wei
- Solé-Casals Jordi
- Wierzchoń Michał
- AGHBroniec-Wójcik Anna
- AGHAugustyniak Piotr
- Marozas Vaidotas
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150603 |
|---|---|
| Data dodania do BaDAP | 2024-01-11 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MeMeA57477.2023.10171931 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario.