ORCID Profile
0000-0002-4694-4926
Current Organisations
University of Southern Queensland
,
Flinders University
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Biomedical Engineering Not Elsewhere Classified | Clinical Engineering | Cloud computing | Cybersecurity and privacy | System and network security | Biomedical Engineering | Engineering/Technology Instrumentation |
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Hindawi Limited
Date: 21-10-2022
DOI: 10.1155/2022/5606833
Abstract: The mobile car parking (MCP) app provides users to locate a car park and park his/her car, which is expected to contribute to sustainable transportation. Previous mobile phone-based studies have also advocated that mobile apps create a social network among the users. However, MCP studies have yet not addressed the notion of such social capital which is expected to contribute to the sustainable transportation in a city. MCP, in fact, can also create a communication network among car owners, drivers, and car park owners and offer a unique base for studying social capital and resource utilization opportunities. To reduce the gap in existing studies, in this study, an android-based MCP app was developed as a tool to study the components of social capital. After developing the MCP app, two focus group studies were conducted to explore the components an MCP app can provide. Lastly, we linked the social capital components to the sustainable transportation goals. This research pinpointed several social capital components that are addressable to sustainable transportation, including information exchange, communication, connectedness, time-saving capabilities, mobility, coordination, plan-based journeys, and an opportunity to generate income from the social network. Moreover, very few studies have reported that social capital empirically contributes to time-saving capabilities, mobility, sustainable transportation through improved coordination in car parking management, and improved journey based on car parking decisions which are new contributions to social capital. This research is significant, as most of the mobile-based social capital research concentrated on bonding, bridging, and networking among social network members. However, this research has expanded the components of social capital in many directions. Furthermore, from the research methodological perspective, this research adopts a new approach through a combination of experimental research and focus group study.
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 2013
Publisher: Informa UK Limited
Date: 15-05-2014
DOI: 10.1080/10255842.2012.683428
Abstract: The biological microenvironment is interrupted when tumour masses are introduced because of the strong competition for oxygen. During the period of avascular growth of tumours, capillaries that existed play a crucial role in supplying oxygen to both tumourous and healthy cells. Due to limitations of oxygen supply from capillaries, healthy cells have to compete for oxygen with tumourous cells. In this study, an improved Krogh's cylinder model which is more realistic than the previously reported assumption that oxygen is homogeneously distributed in a microenvironment, is proposed to describe the process of the oxygen diffusion from a capillary to its surrounding environment. The capillary wall permeability is also taken into account. The simulation study is conducted and the results show that when tumour masses are implanted at the upstream part of a capillary and followed by normal tissues, the whole normal tissues suffer from hypoxia. In contrast, when normal tissues are ahead of tumour masses, their pO2 is sufficient. In both situations, the pO2 in the whole normal tissues drops significantly due to the axial diffusion at the interface of normal tissues and tumourous cells. As the existence of the axial oxygen diffusion cannot supply the whole tumour masses, only these tumourous cells that are near the interface can be partially supplied, and have a small chance to survive.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: MDPI AG
Date: 24-02-2009
DOI: 10.3390/S90201259
Publisher: Elsevier BV
Date: 11-2017
Publisher: MDPI AG
Date: 29-11-2021
DOI: 10.3390/S21237972
Abstract: The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.
Publisher: Elsevier BV
Date: 2009
DOI: 10.1016/J.COMPBIOMED.2008.10.007
Abstract: In this paper, we present a new method to identify anesthetic states based on routinely recorded electroencephalogram (EEG). The identification of anesthesia stages are conducted using fast Fourier transform (FFT) and modified detrended fluctuation analysis (DFA) method. Simulation results demonstrate that this new method can clearly discriminate all five anesthesia states: very deep anesthesia, deep anesthesia, moderate anesthesia, light anesthesia and awake.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 10-2012
Publisher: Elsevier BV
Date: 2010
Publisher: IEEE
Date: 04-2008
Publisher: Medknow
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: IEEE
Date: 12-2008
Publisher: Springer Science and Business Media LLC
Date: 31-08-2010
DOI: 10.1007/S13246-010-0027-3
Abstract: In this study, we consider different conductivity values based on tissue location in a human head model. We implement local conductivity (LC) to compute head surface potentials in three-, four-layered spherical and realistic head models using finite element method (FEM). Implementing LC for all head models, we obtain significant scalp potential variations in the term of relative difference measurement (RDM) and magnification (MAG) values with a maximum of 2.03±1.81 and 8.27±6.36, respectively. We also investigate the effects of conductivity variations (CVs) of head tissue layer on scalp potentials and find a maximum of 2.15±1.93 RDM and 8.57±6.61 MAG values. Our study concludes that it is important to assign LC to each tissue and it is also important to assign appropriate conductivity value in the construction of a head model for achieving accurate scalp potentials.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Hindawi Limited
Date: 08-09-2022
DOI: 10.1155/2022/1992596
Abstract: Schizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called “SchizoGoogLeNet” that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.
Publisher: Springer Science and Business Media LLC
Date: 05-01-2016
DOI: 10.1007/S13246-015-0414-X
Abstract: The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69% of the whole data and 29.5% of the computation time but achieves a 94.5% classification accuracy. The channel selection method based on the GE difference also gains a 91.67% classification accuracy by using only 29.69% of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.
Publisher: IEEE
Date: 2003
Publisher: Inderscience Publishers
Date: 2011
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.COMPBIOMED.2016.09.003
Abstract: Cloud computing was introduced as an alternative storage and computing model in the health sector as well as other sectors to handle large amounts of data. Many healthcare companies have moved their electronic data to the cloud in order to reduce in-house storage, IT development and maintenance costs. However, storing the healthcare records in a third-party server may cause serious storage, security and privacy issues. Therefore, many approaches have been proposed to preserve security as well as privacy in cloud computing projects. Cryptographic-based approaches were presented as one of the best ways to ensure the security and privacy of healthcare data in the cloud. Nevertheless, the cryptographic-based approaches which are used to transfer health records safely remain vulnerable regarding security, privacy, or the lack of any disaster recovery strategy. In this paper, we review the related work on security and privacy preserving as well as disaster recovery in the eHealth cloud domain. Then we propose two approaches, the Security-Preserving approach and the Privacy-Preserving approach, and a disaster recovery plan. The Security-Preserving approach is a robust means of ensuring the security and integrity of Electronic Health Records, and the Privacy-Preserving approach is an efficient authentication approach which protects the privacy of Personal Health Records. Finally, we discuss how the integrated approaches and the disaster recovery plan can ensure the reliability and security of cloud projects.
Publisher: Elsevier BV
Date: 07-2014
DOI: 10.1016/J.CMPB.2014.04.001
Abstract: This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and s le entropy (S En) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals. Experimental results show that feature extraction using the FWHVA is faster than that of S En and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on S En for time series classification.
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.JNEUMETH.2018.11.014
Abstract: Electroencephalogram (EEG) signals are important for brain health monitoring applications. Characteristics of EEG signals are complex, being non-stationarity, aperiodic and nonlinear in nature. EEG signals are a combination of sustained oscillation and non-oscillation transients that are challenging to deal with using linear approaches. This research proposes a new scheme based on a tunable Q-factor wavelet transform (TQWT) and a statistical approach to analyse various EEG recordings. Firstly, the proposed method decompose EEG signals into different sub-bands using the TQWT method, which is parameterized by its Q-factor and redundancy. This method depends on the resonance of a signal, instead of frequency or scaling as in the Fourier and wavelet transforms. Secondly, using a statistical feature extraction on the sub-bands to ide each sub-band into n windows, and then extract several statistical features from each window. Finally, the extracted features are forwarded to a bagging tree (BT), k nearest neighbor (k-NN), and support vector machine (SVM) as classifiers to evaluate the performance of the proposed feature extraction technique. The proposed method is tested on two different EEG databases: Bonn University database and Born University database. The experimental results demonstrate that the proposed feature extraction algorithm with thek-NN classifier produces the best performance compared with the other two classifiers. Comparison with existing methods: In order to further evaluate the performances, the proposed scheme is compared with the other existing methods in terms of accuracy. The results prove that the proposed TQWT based feature extraction method has great potential to extract discriminative information from brain signals. The outcomes of the proposed technique can assist doctors and other health experts to identify ersified EEG categories.
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.CMPB.2015.01.002
Abstract: The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA s les for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature.
Publisher: IGI Global
Date: 07-2009
Abstract: XML documents usually contain private information that cannot be shared by every user communities. It is widely used in web environment. XML database is becoming increasingly important since it consists of XML documents. Several applications for supporting selective access to data are available over the web. Usage control has been considered as the next generation access control model with distinguishing properties of decision continuity. It has been proven efficient to improve security administration with flexible authorization management. Object-oriented database systems represent complex data structure and XML databases may be stored in the objects-oriented database system. Therefore authorization models for XML databases could be used the same the models as object-oriented databases. In this paper, we propose usage control models to access XML databases and compare with an authorization model designed for object-oriented databases. We have analyzed the characteristics of various access authorizations and presented detailed models for different kinds of authorizations. Finally, comparisons with related works are analyzed.
Publisher: Springer Science and Business Media LLC
Date: 21-11-2014
DOI: 10.1007/S13246-014-0309-2
Abstract: This paper introduces a new method addressing depth of anaesthesia (DoA) assessment for real-time monitoring. The new method uses a combination of phase and litude of electroencephalogram (EEG) signals to assess the DoA level. A strong analytical signal transform is applied to extract the phase and litude information of the recorded EEG signals. Based on the extracted features from the EEG signal in each different frequency band, a new DoA index is developed. The proposed new DoA index is evaluated using data from adult patients in an age range from 22 to 75 years. The results show that the new DoA index is able to detect the changing pattern of EEG signals early and agree with the clinical notes of an attending anaesthetist. The results are also closely correlated with the popular BIS index. Furthermore, the proposed new DoA index is able to detect the state changes earlier than the BIS index.
Publisher: Springer International Publishing
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 2012
Publisher: MDPI AG
Date: 06-05-2020
DOI: 10.3390/EN13092307
Abstract: To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional c uses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two c uses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.
Publisher: Elsevier BV
Date: 11-2009
Publisher: IEEE
Date: 08-2007
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4824993
Publisher: National Taiwan University
Date: 17-03-2014
DOI: 10.4015/S1016237214500409
Abstract: This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 06-2012
DOI: 10.1016/J.COMPBIOMED.2012.03.004
Abstract: This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT). The best Daubechies wavelet and their ranking value p are optimally determined to identify different states of anaesthesia. A statistic normalisation process is then carried out to re-scale data and compute the hypnotic depth of anaesthesia. Finally, a new function ZDoA is proposed to compute a DoA index which corresponds one of the five depths of anaesthesia states to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Simulation results based on real anaesthetised EEGs demonstrate that the new index generally parallels the BIS index. In particular, the ZDoA index is often faster than the BIS index to react to the transition period between consciousness and unconsciousness for this data set. A Bland-Altman plot indicates a 95.23% agreement between the ZDoA and BIS indices. The ZDoA trend is responsive, and its movement is consistent with the clinically observed and recorded changes of the patients.
Publisher: Elsevier BV
Date: 2023
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2014
Publisher: Elsevier BV
Date: 09-2014
Publisher: Springer Science and Business Media LLC
Date: 27-02-2009
Publisher: IEEE
Date: 2007
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 03-2007
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 03-2014
DOI: 10.1016/J.CMPB.2013.12.020
Abstract: Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three erse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE
Date: 07-2010
Publisher: Springer Science and Business Media LLC
Date: 28-01-2016
Publisher: IEEE
Date: 07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: IEEE
Date: 07-2010
Publisher: Springer Science and Business Media LLC
Date: 03-2010
DOI: 10.1007/S13246-010-0009-5
Abstract: In this study, we propose a stochastic method to analyze the effects of inhomogeneous anisotropic tissue conductivity on electroencephalogram (EEG) in forward computation. We apply this method to an inhomogeneous and anisotropic spherical human head model. We apply stochastic finite element method based on Legendre polynomials, Karhunen-Loeve expansion and stochastic Galerkin methods. We apply Volume and Wang's constraints to restrict the anisotropic conductivities for both the white matter (WM) and the skull tissue compartments. The EEGs resulting from deterministic and stochastic FEMs are compared using statistical measurement techniques. Based on these comparisons, we find that EEGs generated by incorporating WM and skull inhomogeneous anisotropic tissue properties in idually result in an average of 56.5 and 57.5% relative errors, respectively. Incorporating these tissue properties for both layers together generate 43.5% average relative error. Inhomogeneous scalp tissue causes 27% average relative error and a full inhomogeneous anisotropic model brings in an average of 45.5% relative error. The study results demonstrate that the effects of inhomogeneous anisotropic tissue conductivity are significant on EEG.
Publisher: IEEE
Date: 07-2010
Publisher: Institution of Engineering and Technology (IET)
Date: 09-2007
Publisher: Springer International Publishing
Date: 13-10-2015
DOI: 10.1007/978-3-319-10984-8_8
Abstract: Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.
Publisher: Elsevier BV
Date: 04-2017
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Science Publications
Date: 11-2011
Publisher: Elsevier BV
Date: 11-2018
Publisher: Inderscience Publishers
Date: 2013
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2011
Publisher: Elsevier BV
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 26-05-2010
DOI: 10.1007/S13246-010-0015-7
Abstract: The accuracy of an electroencephalography (EEG) forward problem partially depends on the head tissue conductivities. These conductivities are anisotropic and inhomogeneous in nature. This paper investigates the effects of conductivity uncertainty and analyses its sensitivity on an EEG forward problem for a spherical and a realistic head models. We estimate the uncertain conductivities using an efficient constraint based on an optimization method and perturb it by means of the volume and directional constraints. Assigning the uncertain conductivities, we construct spherical and realistic head models by means of a stochastic finite element method for fixed dipolar sources. We also compute EEG based on the constructed head models. We use a probabilistic sensitivity analysis method to determine the sensitivity indexes. These indexes characterize the conductivities with the most or the least effects on the computed outputs. These results demonstrate that conductivity uncertainty has significant effects on EEG. These results also show that the uncertain conductivities of the scalp, the radial direction of the skull and transversal direction in the white matter are more sensible.
Publisher: MDPI AG
Date: 15-08-2022
DOI: 10.3390/S22166099
Abstract: This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland–Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland–Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/51.887250
Publisher: Elsevier BV
Date: 12-2011
DOI: 10.1016/J.CMPB.2010.11.014
Abstract: This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random s ling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.
Publisher: IEEE
Date: 05-2011
Start Date: 01-2006
End Date: 11-2010
Amount: $135,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded Activity