Conference Papers

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Author: search.filters.author.Acharya, A.U.

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    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.
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    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.
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    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.
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    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.