Global Biomedical Technologies Inc.

CA, United States

Global Biomedical Technologies Inc.

CA, United States
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Giri D.,SIM University | Rajendra Acharya U.,Ngee Ann Polytechnic | Rajendra Acharya U.,University of Malaya | Martis R.J.,Ngee Ann Polytechnic | And 4 more authors.
Knowledge-Based Systems | Year: 2013

Coronary Artery Disease (CAD) is the narrowing of the blood vessels that supply blood and oxygen to the heart. Electrocardiogram (ECG) is an important cardiac signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insights into the state of health and nature of the disease afflicting the heart. However, it is very difficult to perceive the subtle changes in ECG signals which indicate a particular type of cardiac abnormality. Hence, we have used the heart rate signals from the ECG for the diagnosis of cardiac health. In this work, we propose a methodology for the automatic detection of normal and Coronary Artery Disease conditions using heart rate signals. The heart rate signals are decomposed into frequency sub-bands using Discrete Wavelet Transform (DWT). Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were applied on the set of DWT coefficients extracted from particular sub-bands in order to reduce the data dimension. The selected sets of features were fed into four different classifiers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN) and K-Nearest Neighbor (KNN). Our results showed that the ICA coupled with GMM classifier combination resulted in highest accuracy of 96.8%, sensitivity of 100% and specificity of 93.7% compared to other data reduction techniques (PCA and LDA) and classifiers. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of CAD with higher accuracy. © 2012 Elsevier B.V. All rights reserved.


Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | Vinitha Sree S.,CA Technologies | Swapna G.,Government Engineering College | And 3 more authors.
Knowledge-Based Systems | Year: 2013

Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform by visual inspection, thus opening up a vast research area for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. Moreover, the EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Hence, it is necessary to develop a Computer Aided Diagnostic (CAD) system to automatically identify the normal and abnormal activities using minimum number of highly discriminating features in classifiers. It has been found that nonlinear features are able to capture the complex physiological phenomena such as abrupt transitions and chaotic behavior in the EEG signals. In this review, we discuss various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail. We also briefly present the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting. © 2013 Elsevier B.V. All rights reserved.


Acharya U.R.,Ngee Ann Polytechnic | Sree S.V.,Nanyang Technological University | Suri J.S.,Global Biomedical Technologies Inc. | Suri J.S.,Idaho State University
International Journal of Neural Systems | Year: 2011

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique. © 2011 World Scientific Publishing Company.


Acharya U.R.,Ngee Ann Polytechnic | Sree S.V.,Global Biomedical Technologies Inc. | Ang P.C.A.,Ngee Ann Polytechnic | Yanti R.,Ngee Ann Polytechnic | And 2 more authors.
International Journal of Neural Systems | Year: 2012

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy. © 2012 World Scientific Publishing Company.


Mookiah M.R.K.,Ngee Ann Polytechnic | Rajendra Acharya U.,Ngee Ann Polytechnic | Lim C.M.,Ngee Ann Polytechnic | Petznick A.,Singapore Eye Research Institute | And 2 more authors.
Knowledge-Based Systems | Year: 2012

Eye images provide an insight into important parts of the visual system, and also indicate the health of the entire human body. Glaucoma is one of the most common causes of blindness. It is a disease in which fluid pressure in the eye increases gradually, damaging the optic nerve and causing vision loss. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods. These methods are expensive and hence a novel low cost automated glaucoma diagnosis system using digital fundus images is proposed. The paper discusses the system for the automated identification of normal and glaucoma classes using Higher Order Spectra (HOS) and Discrete Wavelet Transform (DWT) features. The extracted features are fed to the Support Vector Machine (SVM) classifier with linear, polynomial order 1, 2, 3 and Radial Basis Function (RBF) to select the best kernel function for automated decision making. In this work, SVM classifier with kernel function of polynomial order 2 was able to identify the glaucoma and normal images automatically with an accuracy of 95%, sensitivity and specificity of 93.33% and 96.67% respectively. Finally, we have proposed a novel integrated index called Glaucoma Risk Index (GRI) which is made up of HOS and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images. © 2012 Elsevier B.V. All rights reserved.


Patent
Global Biomedical Technologies Inc. | Date: 2013-07-12

In one embodiment, a selectively-releasable adhesive includes a base adhesive compound and a releasing compound that is blended with the base adhesive compound, the releasing compound being capable of decreasing the adhesive strength of the base adhesive compound when a releasing agent is applied to the selectively-releasable adhesive.


Trademark
Global Biomedical Technologies Inc. | Date: 2013-05-14

Adhesives and glues for industrial and commercial use.


Trademark
Global Biomedical Technologies Inc. | Date: 2013-01-09

Adhesives for use in the manufacture of the adhesive bandages. Adhesive bandages; Adhesive tapes for medical purposes.


Trademark
Global Biomedical Technologies Inc. | Date: 2013-01-09

Adhesives for use in the manufacture of the adhesive bandages.


Trademark
Global Biomedical Technologies Inc. | Date: 2013-01-15

Computer programs for use in medical image analysis, diagnosis, and treatment, namely, software for processing digital images of the anatomy for analysis, diagnosis and treatment, and software for processing digital images of the anatomy to add additional visual elements to the digital images for enhanced analysis, diagnosis and treatment; computer software for creating searchable medical diagnosis and treatment databases.

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