Shahab Abdulla

Robust internal model control for depth of anaesthesia

This paper investigates the depth of anaesthesia control problem during a surgery, where paralytic, analgesic and hypnotic are regulated by means of monitored administration of specific drugs. A robust internal model controller (RIMC) based on the Bispectral Index (BIS) is proposed. The controller compares the measured BIS with its input reference to provide the expected propofol concentration, and then the controller manipulates the anaesthetic propofol concentration entering the anaesthetic system to achieve the desired BIS value. This study develops patient dose-response models and provides an adequate drug administration regimen to avoid under or over dosing of patients. Numerical simulations illustrate that the RIMC performed better than the traditional PID controller. The robust performance of the two controllers is evaluated for a wide range of patient models by varying in patient parameters. The other relative performance is also compared for different BIS step settings.

The design and investigation of model based internal model control for the regulation of hypnosis

The manual control of anaesthesia is still the dominant practice during surgery. An increasing number of studies have been conducted to explore the possibility of automating this process. The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These discrepancies are translated into the differences in pharmacokinetics (PK), and pharmacodynamics (PD). This study develops patient dose-response models and provides an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated and compared with traditional PID controller. The performance is confirmed in our simulation.

Depth of anaesthesia control investigation using robust deadbeat control technique

This paper investigates the depth of anaesthesia (DoA) control system using robust deadbeat technique. We propose to apply deadbeat control technique and develop a robust controller. The proposed robust control system with a deadbeat controller is evaluated in simulation. The performance is compared with that of a traditional control system with a PID controller and a control system with an internal model (IMC) controller. The results show that the proposed scheme has about 15% less overshoot, shorter settling time (about 1.5 minutes shorter) and more robust to disturbances caused by parameter changes. In addition, the proposed method is easy to design and impalement.

The effects of time-delay on feedback control of depth of anesthesia

This paper presents a new procedure to find the impact of the time-delay (TD) of the patient and instrumentations such as bispectral index (BIS) monitor on closed-loop control of depth of anaesthesia during surgery. In the current work, the TD is estimated using Smith Predictive control technique. The method is validated with measured BIS signals in simulation. The results showed that the proposed procedure improves the performance of the closed-loop system for reference tracking and overall stability, and the proposed method has less overshoot, shorter settling time and is more robust to disturbances.

Depth of anaesthesia patient models and control

During surgery, the anaesthetist carefully controls the delivery of anaesthesia given to the patient in an effort to attain and maintain a consistent and adequate level of anaesthetic depth. The aim of this work is to design an internal model control (IMC) for anaesthesia depth, and to test it by simulation with clinical data. This study uses an internal model control structure for the adjustment of Bispectral Index (BIS). Performance of the two controllers has been studied for a step change in BIS, measured disturbances in the measured variables. In this study the simulation shows that the internal model control performed better than the PID controller.

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

This study proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic electroencephalogram (EEG) signals. This study employs the OA to determine representative sample points from the original EEG data and then applies periodogram (PD), autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k -nearest neighbour (k-NN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus, the findings of this study prove that the proposed OA-based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.

Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images

Due to the plate formats and multiform outdoor illumination conditions during the image acquisition phase, it is challenging to find effective license plate detection (LPD) method. This paper aims to develop a new detection method for identifying vehicle license plates under low quality images using image processing techniques. In this research, a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) are used to detect license plates (LPs) regions. The method achieves a very high accuracy for detecting LP number from one vehicle image. The proposed method was tested and trained with the images from 630 and 400 vehicles, respectively. The images involve many difficult conditions, such as low/high contrast, dusk, dirt, fogy, and distortion problems. The experimental results demonstrate very satisfactory performance for LP detection in term of speed and accuracy, and were better than the most of the existing methods. The processing time for the whole testing LPD system was about 1.63 seconds to 2 seconds. The overall probability detection, precision, and f-measurement are 98.56%, 95.9% and 97.19%, respectively; with false positive rate 5.6%.

Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction.

Brain signals refer to electroencephalogram (EEG) data that contain the most important information in the human brain, which are non-stationary and nonlinear in nature. EEG signals are a mixture of sustained oscillation and non-oscillatory transients that are difficult to deal with by linear methods. This paper proposes a new technique based on a tunable Q-factor wavelet transform (TQWT) and statistical method (SM), denoted as TQWT-SM, to analyze epileptic EEG recordings. Firstly, EEG signals are decomposed into different sub—bands by the TWQT method, which is parameterized by its Q-factor and redundancy. This approach depends on the resonance of signals, instead of frequency or scales as the Fourier and wavelet transforms do. Secondly, each type of the sub-band vector is divided into n windows, and 10 statistical features from each window are extracted. Finally all the obtained statistical features are forwarded to a k nearest neighbor (k-NN) classifier to evaluate the performance of the proposed TQWT-SM method. The TQWT-SM features extraction method achieves good experimental results for the seven different epileptic EEG binary-categories by the k-NN classifier, in terms of accuracy (Acc), Matthew’s correlation coefficient (MCC), and F score (F1). The outcomes of the proposed technique can assist the experts to detect epileptic seizures.

Epileptic seizures detection in EEGs blending frequency domain with information gain technique

AbstractThis paper proposes a new algorithm which combines the information in frequency domain with the Information Gain (InfoGain) technique for the detection of epileptic seizures from electroencephalogram (EEG) data. The proposed method consists of four main steps. Firstly, in order to investigate which method is most suitable to decompose the EEG signals into frequency bands, we implement separately a fast Fourier transform (FFT) or discrete wavelet transform (DWT). Secondly, each band is partitioned into k windows and a set of statistical features are extracted from each window. Thirdly, the InfoGain is used to rank the extracted features and the most important ones are selected. Lastly, these features are forwarded to a least square support vector machine (LS-SVM) classifier to classify the EEG. This scheme is implemented and tested on a benchmark EEG database and also compared with other existing methods, based on some performance evaluation measures. The experimental results show that the proposed FFT combined with InfoGain method can generate better performance than the DWT method. This method achieves 100% accuracy for five different pairs: healthy people with eyes open (z) versus epileptic patients with activity seizures (s); healthy people with eyes closed (o) versus s; epileptic patients with free seizures (n) versus s; patients with free seizures epileptic (f) versus s; and z versus o. The accuracies obtained for two other pairs, (o vs. n) and (z vs. f), are 95.62 and 88.32%, respectively. These two pairs have more similarities with each other, leading to a lower level of accuracy. The proposed approach outperforms six other reported methods and achieves an 11.9% improvement. Finally, it can be concluded that the proposed FFT combined with InfoGain method has the capacity to detect epileptic seizures in EEG most effectively.

A New Approach to Spread-spectrum OFDM.

Orthogonal frequency division multiplexing (OFDM) systems are reviewed and the Shannon bound is dis- cussed as a criterion of efficient spectrum use and a design criterion. The problem of efficient sharing of spectrum by wireless communication systems is discussed and combined use of direct-sequence spread- spectrum (DSSS) coding and OFDM is proposed as an approach which can achieve efficient spectrum sharing. A system which enables DSSS, with codes from the Galois field of order f where f is a prime larger than 2, to be used efficiently in conjunction with OFDM is then defined, analysed, and implemented. Experiments with this system are described.