Faculty Publications
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Item Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example.(Compact storage of medical images with patient information) Acharya, A.U.; Anand, D.; P, S.B.; C, N.U.2001Item A novel visualization technique for voluminous ECG data acquired over several hours is presented. The classified data is displayed in a sector graph, with a menu driven hierarchical display strategy, which progressively unfolds greater details for chosen intervals. A color code is employed to identify different types of abnormalities. Provision is made for fine tuning the classification. © 2002 Elsevier Science Ltd. All rights reserved.(Comprehensive visualization of cardiac health using electrocardiograms) Acharya, A.U.; Subbanna Bhat, P.; Niranjan, U.C.2002Item The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%. © IFMBE: 2004.(Classification of cardiac abnormalities using heart rate signals) Acharya, A.U.; Kumar, A.; Subbanna Bhat, P.; Lim, C.M.; Iyengar, S.S.; Kannathal, N.; Krishnan, S.M.2004Item Automated identification of diabetic retinopathy stages using digital fundus images(2008) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.; Lim, C.M.; Kagathi, M.Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p<0.0001) with distinct mean±SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%. © 2007 Springer Science+Business Media, LLC.Item Epileptic EEG detection using neural networks and post-classification(2008) Patnaik, L.M.; Manyam, O.K.Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet co-efficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained. © 2008 Elsevier Ireland Ltd. All rights reserved.Item Yoga techniques as a means of core stability training(2009) Omkar, S.N.; Vishwas, S.Core stability in general involves the muscular control required around the lumbar spine to maintain functional stability. Stability and movement are critically dependent on the coordination of all the muscles surrounding the lumbar spine. This paper aims to show that an age-old yoga practice, called Uddhyana Bhanda and Nouli, is an effective means of core stability. © 2007 Elsevier Ltd. All rights reserved.Item Automatic identification of diabetic maculopathy stages using fundus images(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets. © 2009 Informa Healthcare USA, Inc.Item Efficient storage and transmission of digital fundus images with patient information using reversible watermarking technique and error control codes(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.; Kumar, M.Handling of patient records is increasing overhead costs for most of the hospitals in this digital age. In most hospitals and health care centers, the patient text information and corresponding medical images are stored separately as different files. There is a possibility of mishandling the text file containing patient history. We are proposing a novel method for the compact storage and transmission of patient information with the medical images. In this technique, we are using a reversible watermarking technique to hide the patient information within the retinal fundus image. There is a possibility that these medical images, which carry patient information, can get corrupted by the noise during the storage or transmission. The safe recovery of patient information is important in this situation. So, to recover the maximum amount of text information in the noisy environment, the encrypted patient information is coded with error control coding (ECC) techniques. The performance of three types of ECC for various levels of salt & pepper (S & P) noise is tabulated for a specific example. The proposed system is more reliable even in a noisy environment and saves memory. © 2008 Springer Science+Business Media, LLC.Item Particle deposition in human respiratory system: Deposition of concentrated hygroscopic aerosols(2009) Varghese, S.K.; Gangamma, S.In the nearly saturated human respiratory tract, the presence of water-soluble substances in the inhaled aerosols can cause change in the size distribution of the particles. This consequently alters the lung deposition profiles of the inhaled airborne particles. Similarly, the presence of high concentration of hygroscopic aerosols also affects the water vapor and temperature profiles in the respiratory tract. A model is presented to analyze these effects in human respiratory system. The model solves simultaneously the heat and mass transfer equations to determine the size evolution of respirable particles and gas-phase properties within human respiratory tract. First, the model predictions for nonhygroscopic aerosols are compared with experimental results. The model results are compared with experimental results of sodium chloride particles. The model reproduces the major features of the experimental data. The water vapor profile is significantly modified only when a high concentration of particles is present. The model is used to study the effect of equilibrium assumptions on particle deposition. Simulations show that an infinite dilution solution assumption to calculate the saturation equilibrium over droplet could induce errors in estimating particle growth. This error is significant in the case of particles of size greater than 1 ?m and at number concentrations higher than 105/cm3. © 2009 Informa UK Ltd.Item Spectrophotometric determination of platinum(IV) in alloys, complexes, environmental, and pharmaceutical samples using 4-[N,N-(diethyl)amino] benzaldehyde thiosemicarbazone(2010) Naik, P.P.; Karthikeyan, J.; Nityananda Shetty, A.N.4-[N,N-(Diethyl)amino] benzaldehyde thiosemicarbazone (DEABT) is proposed as an analytical reagent for the spectrophotometric determination of platinum(IV). The DEABT forms 1:2 yellow complex with Pt(IV), which is sparingly soluble in water and completely soluble in water-ethanol-DMF medium. The Pt(IV)-DEABT complex shows maximum absorbance at 405 nm. Beer's law is valid up to 7.80 ?g cm-3, and optimum concentration range for the determination of platinum(IV) is 0.48-7.02 ?g cm-3. The molar absorptivity and Sandell's sensitivity of the method are found to be 1.755 × 104 dm3 mol-1 cm-1 and 0.0012 ?g cm-2, respectively. The relative error and coefficient of variation (n=6) for the method does not exceed ±0.43% and 0.35%, respectively. Since the method tolerates a number of metal ions commonly associated with platinum, it can be employed for the determination of platinum in environmental samples, pharmaceutical samples, alloys, catalysts, and complexes. The method is rapid as the Pt(IV)-DEABT complex is soluble in water-ethanol-DMF medium and not requiring any time consuming extraction method for the complex. © 2010 Springer Science+Business Media B.V.