—Surface electromyography (sEMG) records muscle activities from the surface of muscles, which off... more —Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data is translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this article is to disseminate the role of PCA in conjunction with quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed. Index Terms—Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.
— This paper presents a two-class electroencephalography (EEG)-based classification for classifyi... more — This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications. Index Terms—electroencephalography (EEG), driver fatigue, autoregressive (AR) model, independent component analysis, entropy rate bound minimization, Bayesian neural network.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
This paper presents research that investigated the effects of mental fatigue on brain activity us... more This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
This paper presents research that investigated the effects of mental fatigue on brain activity as... more This paper presents research that investigated the effects of mental fatigue on brain activity associated with eyes open and eyes closed conditions. The changes associated with electroencephalography (EEG) alpha wave activity (8-13 Hz) during eye closure has previously been shown to be an effective strategy for switching and activating devices as an environmental control system (ECS) designed for people with severe disability like spinal cord injury (SCI). The results showed that switching times did increase due to fatigue, however, these increases were not large (around 1 second longer to switch) and this difference was not significant. When baselines were readjusted taking into account the change in alpha wave activity due to the fatigue, switching reduced to times typically seen when the person was alert. Error rates were similar between the alert and fatigue sates. Implications of these results for a hands-free ECS are discussed.
This paper describes the design of a novel sliding mode controller for tracking of robotic manipu... more This paper describes the design of a novel sliding mode controller for tracking of robotic manipulators. By using a proportional-integral combination of the sliding function in the boundary layer, control chattering is eliminated without deterioration of system robustness. Theoretical developments are verified by simulation results for the tracking problem of a 2 DOF robotic manipulator.
Fouling is a common but serious problem encountered during membrane filtration. It leads to perme... more Fouling is a common but serious problem encountered during membrane filtration. It leads to permeability decline, higher energy demands for maintaining a constant permeate flux and eventually requires the membrane separation process to be completely stopped for physical or chemical cleaning of the membrane filtration system. Previous studies associated with membrane cleaning involving complex models, statistically designed experiments and trial
A membrane system only has a limited operational lifetime, whereby it becomes so severely fouled ... more A membrane system only has a limited operational lifetime, whereby it becomes so severely fouled that continued operation must be stopped. In the cross-flow configuration of membrane filtration of wastewater, both increased cross-flow velocities and decreased operational transmembrane pressures can be used to decrease membrane fouling and extend the life cycle of the membrane separation process. The study found that
Organic fouling on the membrane can be minimised through powdered activated carbon (PAC) usage in... more Organic fouling on the membrane can be minimised through powdered activated carbon (PAC) usage in the submerged membrane reactor to adsorb dissolved organic matter and reduce direct organic loading on the membrane. However, fouling cannot be totally alleviated.A periodic back flush during a membrane filtration process can successfully remove most of the reversible fouling leading to reduced pressure drop and
The development of acute hypotension in a critical care patient causes decreased tissue perfusion... more The development of acute hypotension in a critical care patient causes decreased tissue perfusion, which can lead to multiple organ failures. Existing systems that employ population level prognostic scores to stratify the risks of critical care patients based on hypotensive episodes are suboptimal in predicting impending critical conditions, or in directing an effective goal-oriented therapy. In this work, we propose a sequential pattern mining approach which target novel and informative sequential contrast patterns for the detection of hypotension episodes. Our results demonstrate the competitiveness of the approach, in terms of both prediction performance as well as knowledge interpretability. Hence, sequential patterns-based computational biomarkers can help comprehend unusual episodes in critical care patients ahead of time for early warning systems. Sequential patterns can thus aid in the development of a powerful critical care knowledge discovery framework for facilitating nov...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and ev... more Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop Bayesian neural network detection algorithms to recognize the presence of hypoglycemic episodes. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (1.033+/-0.242 vs. 1.082+/-0.298, P0.06) and increased corrected QT intervals (1.031+/-0.086 vs. 1.060+/-0.084, P0.001). The overall data were organized into a training set (8 patients) and a test set (another 8 patients) randomly selected. Using the optimal Bayesian neural network with ...
Hypoglycemia (low blood glucose) or the fear of
hypoglycemia constitutes a significant barrier t... more Hypoglycemia (low blood glucose) or the fear of
hypoglycemia constitutes a significant barrier to the
achievement of good glycemic control in the insulin treated
diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia
can be detected non-invasively. From a clinical study of six
children with type 1 diabetes (T1D), associated with
hypoglycemic episodes at night, their centroid (centre of
gravity) alpha frequency reduced significantly (P<0.001) and
their centroid theta frequency increased significantly (P<0.02).
The overall data were organized into a training set (3 patients)
and a test set (another 3 patients) randomly selected. Using the
optimal Bayesian neural network which was derived from the
training set with the highest log evidence, the estimated blood
glucose profiles produced a significant correlation (P<0.005)
against measured values in the test set.
Abstract This book focuses on computational intelligence techniques and its applications-fast-gro... more Abstract This book focuses on computational intelligence techniques and its applications-fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical ...
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patien... more Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82\%, 77\%, and 78\%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by t...
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm... more The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm to improve the quality of the original biosensors recordings, which can be used as valuable pre-processing technique such as cross talk removal, artefact reduction etc. Initially, the ill conditioned original surface Electromyography (sEMG) recordings were separated using ICA methods and later they were reconstructed using modified un-mixing matrix. The simulation results showed huge improvement of the original recorded signal after reconstruction. The proposed method has potential applications in various biomedical signal processing techniques.
ABSTRACT Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big c... more ABSTRACT Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of cost reduction. FDI makes it possible to reduce total cost effective of maintenance and thus increase the capacity utilization rates of equipment. Reduction of energy wasting in the system by on time fault detection is another goal. Therefore, this work proposes a new fault detector based on a black box Artificial Neural Network (ANN) model and online support vector machines (SVM) classifier which integrates a dimension reduction scheme to analyze the failure of air fan supply and dampers fault. The key advantage of this algorithm is to make robustness for SVM to recognize a faulty condition with unexpected sensors values. The ANN generates a high accurate model which is based reference for SVM classifier. Now by using this black box model we make possibility of robustness for SVM to increase detection probability. Finally, a series of faulty experimental data are applied to evaluate the effectiveness of the robust classifier. Final results show that online SVM can detect accurately the air supply fan fault and damper fault of a HVAC system with minimum usage data. It is also outperforms offline SVM on such energy systems for classification.
Recently, the literature has witnessed an increasing interest in the study of medical image water... more Recently, the literature has witnessed an increasing interest in the study of medical image watermarking and recovery techniques. In this article, a novel image tamper localization and recovery technique for medical image authentication is proposed. The sparse coding of the Electronic Patient Record (EPR) and the reshaped region of Interest (ROI) is embedded in the transform domain of the Region of Non-Interest (RONI). The first part of the sparse coded watermark is use for saving the patient information along with the image, whereas the second part is used for authentication purpose. When the watermarked image is tampered during transmission between hospitals and medical clinics, the embedded sparse coded ROI can be extracted to recover the tampered image. The experimental results demonstrate the efficiency of the proposed technique in term of tamper correction capability, robustness to attacks, and imperceptibility.
Medical & Biological Engineering & Computing, 2010
This article reports on a study to identify electroencephalography (EEG) signals with potential t... more This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The
This paper deals with the important issue of security and confidentiality of patient information ... more This paper deals with the important issue of security and confidentiality of patient information when exchanging or storing medical images. Steganography has recently been viewed as an alternative or complement to cryptography, as existing cryptographic systems are not perfect due to their vulnerability to certain types of attack. We propose in this paper a new steganography algorithm for hiding patient confidential information. It utilizes Pixel Value Differencing (PVD) to identify contrast regions in the image and a Hamming code that embeds 3 secret message bits into 4 bits of the cover image. In order to preserve the content of the region of interest (ROI), the embedding is only performed using the Region of Non-Interest (RONI).
—Surface electromyography (sEMG) records muscle activities from the surface of muscles, which off... more —Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data is translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this article is to disseminate the role of PCA in conjunction with quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed. Index Terms—Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.
— This paper presents a two-class electroencephalography (EEG)-based classification for classifyi... more — This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications. Index Terms—electroencephalography (EEG), driver fatigue, autoregressive (AR) model, independent component analysis, entropy rate bound minimization, Bayesian neural network.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
This paper presents research that investigated the effects of mental fatigue on brain activity us... more This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
This paper presents research that investigated the effects of mental fatigue on brain activity as... more This paper presents research that investigated the effects of mental fatigue on brain activity associated with eyes open and eyes closed conditions. The changes associated with electroencephalography (EEG) alpha wave activity (8-13 Hz) during eye closure has previously been shown to be an effective strategy for switching and activating devices as an environmental control system (ECS) designed for people with severe disability like spinal cord injury (SCI). The results showed that switching times did increase due to fatigue, however, these increases were not large (around 1 second longer to switch) and this difference was not significant. When baselines were readjusted taking into account the change in alpha wave activity due to the fatigue, switching reduced to times typically seen when the person was alert. Error rates were similar between the alert and fatigue sates. Implications of these results for a hands-free ECS are discussed.
This paper describes the design of a novel sliding mode controller for tracking of robotic manipu... more This paper describes the design of a novel sliding mode controller for tracking of robotic manipulators. By using a proportional-integral combination of the sliding function in the boundary layer, control chattering is eliminated without deterioration of system robustness. Theoretical developments are verified by simulation results for the tracking problem of a 2 DOF robotic manipulator.
Fouling is a common but serious problem encountered during membrane filtration. It leads to perme... more Fouling is a common but serious problem encountered during membrane filtration. It leads to permeability decline, higher energy demands for maintaining a constant permeate flux and eventually requires the membrane separation process to be completely stopped for physical or chemical cleaning of the membrane filtration system. Previous studies associated with membrane cleaning involving complex models, statistically designed experiments and trial
A membrane system only has a limited operational lifetime, whereby it becomes so severely fouled ... more A membrane system only has a limited operational lifetime, whereby it becomes so severely fouled that continued operation must be stopped. In the cross-flow configuration of membrane filtration of wastewater, both increased cross-flow velocities and decreased operational transmembrane pressures can be used to decrease membrane fouling and extend the life cycle of the membrane separation process. The study found that
Organic fouling on the membrane can be minimised through powdered activated carbon (PAC) usage in... more Organic fouling on the membrane can be minimised through powdered activated carbon (PAC) usage in the submerged membrane reactor to adsorb dissolved organic matter and reduce direct organic loading on the membrane. However, fouling cannot be totally alleviated.A periodic back flush during a membrane filtration process can successfully remove most of the reversible fouling leading to reduced pressure drop and
The development of acute hypotension in a critical care patient causes decreased tissue perfusion... more The development of acute hypotension in a critical care patient causes decreased tissue perfusion, which can lead to multiple organ failures. Existing systems that employ population level prognostic scores to stratify the risks of critical care patients based on hypotensive episodes are suboptimal in predicting impending critical conditions, or in directing an effective goal-oriented therapy. In this work, we propose a sequential pattern mining approach which target novel and informative sequential contrast patterns for the detection of hypotension episodes. Our results demonstrate the competitiveness of the approach, in terms of both prediction performance as well as knowledge interpretability. Hence, sequential patterns-based computational biomarkers can help comprehend unusual episodes in critical care patients ahead of time for early warning systems. Sequential patterns can thus aid in the development of a powerful critical care knowledge discovery framework for facilitating nov...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and ev... more Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop Bayesian neural network detection algorithms to recognize the presence of hypoglycemic episodes. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (1.033+/-0.242 vs. 1.082+/-0.298, P0.06) and increased corrected QT intervals (1.031+/-0.086 vs. 1.060+/-0.084, P0.001). The overall data were organized into a training set (8 patients) and a test set (another 8 patients) randomly selected. Using the optimal Bayesian neural network with ...
Hypoglycemia (low blood glucose) or the fear of
hypoglycemia constitutes a significant barrier t... more Hypoglycemia (low blood glucose) or the fear of
hypoglycemia constitutes a significant barrier to the
achievement of good glycemic control in the insulin treated
diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia
can be detected non-invasively. From a clinical study of six
children with type 1 diabetes (T1D), associated with
hypoglycemic episodes at night, their centroid (centre of
gravity) alpha frequency reduced significantly (P<0.001) and
their centroid theta frequency increased significantly (P<0.02).
The overall data were organized into a training set (3 patients)
and a test set (another 3 patients) randomly selected. Using the
optimal Bayesian neural network which was derived from the
training set with the highest log evidence, the estimated blood
glucose profiles produced a significant correlation (P<0.005)
against measured values in the test set.
Abstract This book focuses on computational intelligence techniques and its applications-fast-gro... more Abstract This book focuses on computational intelligence techniques and its applications-fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical ...
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patien... more Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82\%, 77\%, and 78\%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by t...
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm... more The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm to improve the quality of the original biosensors recordings, which can be used as valuable pre-processing technique such as cross talk removal, artefact reduction etc. Initially, the ill conditioned original surface Electromyography (sEMG) recordings were separated using ICA methods and later they were reconstructed using modified un-mixing matrix. The simulation results showed huge improvement of the original recorded signal after reconstruction. The proposed method has potential applications in various biomedical signal processing techniques.
ABSTRACT Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big c... more ABSTRACT Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of cost reduction. FDI makes it possible to reduce total cost effective of maintenance and thus increase the capacity utilization rates of equipment. Reduction of energy wasting in the system by on time fault detection is another goal. Therefore, this work proposes a new fault detector based on a black box Artificial Neural Network (ANN) model and online support vector machines (SVM) classifier which integrates a dimension reduction scheme to analyze the failure of air fan supply and dampers fault. The key advantage of this algorithm is to make robustness for SVM to recognize a faulty condition with unexpected sensors values. The ANN generates a high accurate model which is based reference for SVM classifier. Now by using this black box model we make possibility of robustness for SVM to increase detection probability. Finally, a series of faulty experimental data are applied to evaluate the effectiveness of the robust classifier. Final results show that online SVM can detect accurately the air supply fan fault and damper fault of a HVAC system with minimum usage data. It is also outperforms offline SVM on such energy systems for classification.
Recently, the literature has witnessed an increasing interest in the study of medical image water... more Recently, the literature has witnessed an increasing interest in the study of medical image watermarking and recovery techniques. In this article, a novel image tamper localization and recovery technique for medical image authentication is proposed. The sparse coding of the Electronic Patient Record (EPR) and the reshaped region of Interest (ROI) is embedded in the transform domain of the Region of Non-Interest (RONI). The first part of the sparse coded watermark is use for saving the patient information along with the image, whereas the second part is used for authentication purpose. When the watermarked image is tampered during transmission between hospitals and medical clinics, the embedded sparse coded ROI can be extracted to recover the tampered image. The experimental results demonstrate the efficiency of the proposed technique in term of tamper correction capability, robustness to attacks, and imperceptibility.
Medical & Biological Engineering & Computing, 2010
This article reports on a study to identify electroencephalography (EEG) signals with potential t... more This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The
This paper deals with the important issue of security and confidentiality of patient information ... more This paper deals with the important issue of security and confidentiality of patient information when exchanging or storing medical images. Steganography has recently been viewed as an alternative or complement to cryptography, as existing cryptographic systems are not perfect due to their vulnerability to certain types of attack. We propose in this paper a new steganography algorithm for hiding patient confidential information. It utilizes Pixel Value Differencing (PVD) to identify contrast regions in the image and a Hamming code that embeds 3 secret message bits into 4 bits of the cover image. In order to preserve the content of the region of interest (ROI), the embedding is only performed using the Region of Non-Interest (RONI).
Uploads
hypoglycemia constitutes a significant barrier to the
achievement of good glycemic control in the insulin treated
diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia
can be detected non-invasively. From a clinical study of six
children with type 1 diabetes (T1D), associated with
hypoglycemic episodes at night, their centroid (centre of
gravity) alpha frequency reduced significantly (P<0.001) and
their centroid theta frequency increased significantly (P<0.02).
The overall data were organized into a training set (3 patients)
and a test set (another 3 patients) randomly selected. Using the
optimal Bayesian neural network which was derived from the
training set with the highest log evidence, the estimated blood
glucose profiles produced a significant correlation (P<0.005)
against measured values in the test set.
hypoglycemia constitutes a significant barrier to the
achievement of good glycemic control in the insulin treated
diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia
can be detected non-invasively. From a clinical study of six
children with type 1 diabetes (T1D), associated with
hypoglycemic episodes at night, their centroid (centre of
gravity) alpha frequency reduced significantly (P<0.001) and
their centroid theta frequency increased significantly (P<0.02).
The overall data were organized into a training set (3 patients)
and a test set (another 3 patients) randomly selected. Using the
optimal Bayesian neural network which was derived from the
training set with the highest log evidence, the estimated blood
glucose profiles produced a significant correlation (P<0.005)
against measured values in the test set.