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

  • Author(s): Mohammed Diykh 1, 2 ; Firas Sabar Miften 2 ; Shahab Abdulla 3 ; Khalid Saleh 4 ; Jonathan H. Green 3, 5
    • View affiliations
  • Source: Volume 14, Issue 1, January 2020, p. 128 – 136
    DOI:  10.1049/iet-smt.2018.5393 , Print ISSN 1751-8822, Online ISSN 1751-8830
© The Institution of Engineering and Technology
Received 12/07/2018, Accepted 13/11/2019, Revised 23/10/2019, Published 14/11/2019

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 electroencephalogram (EEG) signal and to design 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.

Inspec keywords: electroencephalography; surgery; medical signal processing; statistical analysis; feature extraction; feature selection; wavelet transforms; neurophysiology; signal denoising; direction-of-arrival estimation; fast Fourier transforms; patient monitoring

Other keywords: efficient depth; DoA values; hybrid transform; wavelet transform; statistical feature extraction; BIS index; DoA index; surgery; DoA assessment; wavelet-Fourier analysis; denoised EEG signal; anaesthetic electroencephalogram signal; feature selection; patient anaesthetic state; automated DoA approach; robust approach; bispectral index monitor; EEG; anaesthesia assessment technique; fast Fourier transform

Subjects: Function theory, analysis; Biology and medical computing; Electrodiagnostics and other electrical measurement techniques; Patient care and treatment; Other topics in statistics; Digital signal processing; Integral transforms; Signal processing and detection; Integral transforms; Bioelectric signals; Electrical activity in neurophysiological processes; Probability theory, stochastic processes, and statistics; Patient care and treatment; Other topics in statistics

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