Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Identification of voice disorders using speech samples(2003) Nayak, J.; Subbanna Bhat, P.This paper attempts to identify pathological disorders of larynx using Wavelet Analysis. Speech samples carry symptoms of disorder in the place of their origin. The speech signal is subjected to wavelet analysis, and the coefficients are used to identify disorders such as Vocal Fold Paralysis. Multilayer Artificial Neural Network is used for classification of normal and affected signals.Item AR modeling of heart rate signals(Institute of Electrical and Electronics Engineers Inc., 2004) Nayak, J.; Subbanna Bhat, P.; Acharya, A.U.; Niranjan, U.C.; Sing, O.W.The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks etc may contain useful information about the nature of disease afflicting the heart. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal is used as the base signal for the highly useful in diagnostics. This paper deals with the analysis of eight cardiac abnormalities using Auto Regressive (AR), modeling technique. The results are tabulated below for specific example. © 2004 IEEE.Item Reliable and robust transmission and storage of medical images with patient information(2004) Nayak, J.; Subbanna Bhat, P.; Kumar, M.S.; Acharya, A.U.A new method for compact storage and transmission of medical images with concealed patient information in noisy environment is evinced. Digital Watermarking is the technique adapted here for interleaving patient information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc.) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient information is coded by Error Correcting Codes (ECC) such as (7,4) Hamming, Bose-Chaudhuri-Hocquenghem (BCH) and Reed Solomon (RS) codes. The noisy scenario is simulated by adding salt and pepper (S&P) noise to the embedded image. For different Signal to Noise Ratio (SNR) of the image, Bit Error Rate (BER) and Number of Character Altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph are evaluated. The performance comparison based on the above parameters is conducted for three types of ECC. It is elicited that coded systems can perform better than the uncoded systems. © 2004 IEEE.Item Reliable transmission and storage of medical images with patient information using error control codes(2004) Nayak, J.; Subbanna Bhat, P.; Kumar, M.S.; Acharya, A.U.A new method for compact storage and transmission of medical Images with concealed patient Information in noisy environment if evinced. Digital Watermarking is the technique adapted here for interleaving patient Information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc.) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient Information Is coded by Error Correcting Codes (ECC) Reed Solomon (RS) codes. The noisy scenario Is simulated by adding salt and pepper (S&P) noise to the embedded Image. For different Signal to Noise Ratio (SNR) of the image, Bit Error Rate (BER) and Number of Character Altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph is evaluated. It Is elicited that coded systems can perform better than the uncoded systems. © 2004 IEEE.Item Hidden Markov model-contourlet Hidden Markov tree based texture segmentation(2004) Raghavendra, B.S.; Subbanna Bhat, P.Contourlets have emerged as a new mathematical tool for image processing and provide compact and decorrelated image representations. Hidden Markov modeling (HMM) of contourlet coefficients is a powerful approach for statistical processing of natural images. In this paper, we extended the hidden Markov modeling framework to contourlets and combined hidden Markov trees (HMT) with hidden Markov model to form HMM-Contourlet HMT model. The model is used for block based multiresolution texture segmentation. The performance of the HMM-Contourlet HMT texture segmentation method is compared with that of HMM-Real HMT and HMM-Complex HMT methods. The HMM-Contourlet HMT method provides superior texture segmentation results and excellent visual performance at small block sizes. © 2004 IEEE.Item Cardiac health diagnosis using wavelet transformation and phase space plots(2005) Acharya, A.U.; Subbanna Bhat, P.; Kannathal, N.; Lim, C.M.; Laxminarayan, S.Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially nonstationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper presents the continuous time wavelet analysis of heart rate variability signal for disease identification. Phase space plots of heart rate signal for a chosen embedding dimension are compared with the wavelet analysis patterns. © 2005 IEEE.Item Shift-invariant image denoising using mixture of laplace distributions in wavelet-domain(2006) Raghavendra, B.S.; Subbanna Bhat, P.In this paper, we propose a new method for denoising of images based on the distribution of the wavelet transform. We model the discrete wavelet coefficients as mixture of Laplace distributions. Redundant, shift invariant wavelet transform is made use of in order to avoid aliasing error that occurs with critically sampled filter bank. A simple Expectation Maximization algorithm is used for estimating parameters of the mixture model of the noisy image data. The noise is considered as zero-mean additive white Gaussian. Using the mixture probability model, the noise-free wavelet coefficients are estimated using a maximum a posteriori estimator. The denoising method is applied for general category of images and results are compared with that of wavelet-domain hidden Markov tree method. The experimental results show that the proposed method gives enhanced image estimation results in the PSNR sense and better visual quality over a wide range of noise variance. © Springer-Verlag Berlin Heidelberg 2006.