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

Diykh, Mohammed; Miften, Firas Sabar; Abdulla, Shahab; Saleh, Khalid; Green, Jonathan H.

doi:10.1049/iet-smt.2018.5393

Cited by 19 publications

(9 citation statements)

References 33 publications

(43 reference statements)

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“…Hence, the proposed model can be utilised as a low cost, and a simple model to predict the DoA using single channel EEG signals. [17] Wavelet technique based on Fourier transform EEG Diykh et al, [35] Complex network-based spectrum technique EEG 86 Chowdhury et al, [23]…”

Section: Discussionmentioning

confidence: 99%

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Alsafy

Diykh

2022

Phys Eng Sci Med

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Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient’s state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.

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Section: Discussionmentioning

confidence: 99%

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Alsafy

Diykh

2022

Phys Eng Sci Med

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“…In this study, we adopted our previous study to segment EEG signals [ 18 , 19 , 20 , 21 ]. Evidently, the proposed method granted a highly satisfactory classification accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…Experts depend on different techniques to capture brain activity and detect seizures, such as electroencephalograms (EEG) and magnetic resonance imaging (MRI) [ 16 , 17 ]. However, researchers are in favour of EEG for epilepsy diagnosis due to it is low cost; it also provides supportive proof of seizures and assists with detection of epilepsy [ 18 , 19 , 20 , 21 ]. In addition, clinical studies have shown that a seizure can leave signs on a patient’s EEG recording even after it occurs.…”

Section: Introductionmentioning

confidence: 99%

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Abdulla¹,

Diykh²,

Alkhafaji³

et al. 2021

Diagnostics

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Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

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“…Dennis et al [ 6 ] gave a summary review of EEG-based brain–computer interface (BCI) and explored the possibility of enhancing neurorehabilitation of people with strokes and other chronic disorders. Mohammed et al [ 7 ] introduced a new method to represent the depth of anesthesia (DOA) as compared to conventional bispectral index (BIS) monitor using wavelet–Fourier analysis (WFA DOA ) on EEG signal.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

Chaiyanan

Iramina

Kaewkamnerdpong

2021

Entropy

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The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.

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

Cited by 19 publications

References 33 publications

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

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