Depth of anaesthesia monitoring: a clinical study comparing BIS and AAI during heart surgery

Tiren, C.; Barr, G.; Anderson, R. E.; Jakobsson, Jan

doi:10.1097/00003643-200406002-00137

Cited by 1 publication

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“…Shepherd et al [33], made a study to assess the clinical effectiveness and cost-effectiveness of BIS, E-Entropy and Narcotrend by comparing each one with standard clinical monitoring, to monitor the DoA in surgical patients undergoing the general anaesthesia. Tiren et al [38], made a clinical study in which the BIS and Entropy monitoring were compared in patients undergoing heart surgery. Their results showed that the BIS indicated deep anaesthesia more accurately than entropy.…”

Section: Introductionmentioning

confidence: 99%

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Robust approach to depth of anaesthesia assessment based on hybrid transform and statistical features

Diykh

Miften

Abdulla

et al. 2020

IET Science, Measurement & Technology

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To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient's anaesthetic state during surgery, a new automated DoA approach was proposed. It applied Wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic EEG signal and to designed a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a Fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA () was developed and compared with the most popular Bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the were also tested, and the obtained results demonstrated that the could indicate the DoA values, while the BIS failed to show valid outputs for those situations.. Auto-generated PDF by ReView IET Science, Measurement & Technology IET Submission R evised M ohammed.docxM ainDocument IET Review Copy Only 2 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.

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Section: Introductionmentioning

confidence: 99%

“…[34,35]. Most of the feature extraction methods were related to using a Fourier transform or a Wavelet transform to pull out the key characteristics of EEG signals [36][37][38]. Those techniques mainly aim to reduce the dimensionality of the EEG data and increase the accuracy of the DoA estimation.…”

Section: Introductionmentioning

confidence: 99%