Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

Jahanseir, Mercedeh; Setarehdan, Seyed Kamaledin; Momenzadeh, Sirous

doi:10.1007/s13246-018-0688-x

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…Vienas iš bendrosios nejautros tikslų -slopinti paciento prisiminimus ir patirtį nuo anestezijos indukcijos iki planuojamo pabudimo [8]. Kasdienėje klinikinėje praktikoje atliekamų intervencijų veiksmingumas gali būti vertinamas pagal tokius fiziologinius rodiklius, kaip arterinis kraujospūdis, kvėpavimo dažnis, širdies susitraukimų dažnis [5]. Šie rodikliai anestezijos metu kinta ir dažnai jų nepageidaujamus pokyčius sukelia tam tikri medikamentai ar jų deriniai, kuriais siekiama, jog pacientas procedūros metu išliktų nesąmoningas.…”

Section: įVadasunclassified

Anestezijos Gylio Monitoravimas Elektroencefalografija Pagrįstomis Sistemomis

Skrabytė¹,

Barzdaitė²

2022

Health Sciences

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Sukeliant bendrąją nejautrą, siekiama, kad pacientas išliktų nesąmoningas visos planuojamos intervencijos metu [8]. Paciento sąmoningumas anestezijos metu gali sukelti tokius psichologinius sutrikimus, kaip nerimas ar potrauminio streso sindromas [4]. Medikamentai, kurie sukelia anestezinį miegą, veikia smegenis. Norint tiksliai įvertinti anestezijos gylį, auksiniu standartu laikomi pokyčiai elektroencefalogramoje (EEG) [8]. Klinikinėje praktikoje dažniausiai naudojamas bispektrinio indekso monitorius (BIS). Atliktų tyrimų duomenimis nustatyta, kad stebint BIS reikšmes operacijos metu, skiriamos anestetikų dozės sumažėja iki 27 proc. ir mažėja intraoperacinio pacientų sąmoningumo rizika [9].

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Section: įVadasunclassified

Anestezijos Gylio Monitoravimas Elektroencefalografija Pagrįstomis Sistemomis

Skrabytė¹,

Barzdaitė²

2022

Health Sciences

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“…Artificial intelligence techniques have been employed to analyze EEG signals to assess DOA [17][18][19][20][21][22][23][24][25][26]. DOA estimation involves two primary tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The second task focuses on directly fitting the BIS, utilizing a more nuanced index to characterize the various levels of anesthesia. In the context of classification, Jahanseir et al [19] proposed a DOA assessment method based on a least squares support vector machine classifier, using the relative power of EEG signal bands and entropy measures. The accuracy was 80% for the classification of four anaesthesia states: awake, light anaesthesia, moderate anaesthesia, and deep anaesthesia.…”

Section: Introductionmentioning

confidence: 99%

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia

Yu,

Zhou,

et al. 2024

J. Neural Eng.

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Objective:Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, accurate and real-time estimation of DOA remains a challenging task. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the electroencephalogram (EEG) signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.Approach:The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.Main results:The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the 5-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the 5-fold cross-validation was 0.82, with an MAE of 5.66.Significance:The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly at https://github.com/YuRui8879/SQI-DOANet.

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Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

Cited by 2 publications

References 48 publications

Anestezijos Gylio Monitoravimas Elektroencefalografija Pagrįstomis Sistemomis

Anestezijos Gylio Monitoravimas Elektroencefalografija Pagrįstomis Sistemomis

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia

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