Faculty Publications
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Item Classification of cardiac abnormalities using heart rate signals: A comparative study(Springer Berlin Heidelberg, 2007) Acharya, R.; Kannathal, N.; Subbanna Bhat, P.S.; Suri, J.S.; Min, L.C.; Spaan, J.A.E.[No abstract available]Item Visualization of cardiac health using electrocardiograms(Springer Berlin Heidelberg, 2007) Acharya, R.; Subbanna Bhat, P.S.; Niranjan, U.C.; Kannathal, N.; Min, L.C.; Suri, J.S.The chapter discusses an efficient and novel method to assist the physician to visualize voluminous cardiac data acquired over several hours. The system uses different colors to identify different types of cardiogram signals. In the display strategy each ECG beat is represented by a grid. The visualization strategy is hierarchical; that is, it provides for viewing of data from different level of abstraction, and the physician can have a top down approach to narrow down the time interval and signal details. This display strategy is extended to sector graph, with a menu driven hierarchical display strategy, which progressively unfolds greater details for chosen intervals. Provision is made for changing the parameters of classification, and thus the physician has the option for fine tuning the classification. © Springer-Verlag Berlin Heidelberg 2007.Item Storage and transmission of cardiac data with medical images(Springer Berlin Heidelberg, 2007) Acharya, R.; Subbanna Bhat, P.S.; Niranjan, U.C.; Kumar, S.; Kannathal, N.; Min, L.C.; Suri, J.S.The landscape of healthcare delivery and medical data management has significantly changed over the last years, as a result of the significant advancements in information and communication technologies. Complementary and/or alternative solutions are needed to meet the new challenges, especially regarding security of the widely distributed sensitive medical information. Digital watermarking is a technique of hiding specific identification data for copyright authentication. The DICOM standard is one method to include demographic information, such as patient information and X-ray exposure facilities, in image data. The DICOM standard is a standard that can be used regularly to record demographic information onto the image data header section. Regarding DICOM format images, information on patients and X-ray exposure facilities can be obtained easily from them. On the other hand, general-purpose image formats, such as the JPEG format, offer no standard that can be used regularly to record demographic information onto the header section. Digital watermark technologies [1-8] can be used to embed demographic information in image data. Digital watermarking have several other uses, such as fingerprinting, authentication, integrity verification purposes, content labeling, usage control and content protection [9, 10]. The efficient utilization of bandwidth of communication channel and storage space can be achieved, when the reduction in data size is done. Recently, Giakoumaki et al, have presented a review of research in the area of medical-oriented watermarking and proposed a wavelet-based multiple watermarking scheme. This scheme aimed to address critical health information management issues, including origin and data authentication, protection of sensitive data, and image archiving and retrieval [11]. Their experimental results on different medical imaging modalities demonstrated the efficiency and transparency of the watermarking scheme. The digital watermarking technique is adapted in this chapter for interleaving patient information with medical images, to reduce storage and transmission overheads. The text data is 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 as well as encryption and results are tabulated for a specific example. Adverse effects of channel induced random errors and burst errors on the text data are countered by employing repetition code, Hamming code and R-S code techniques.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 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 Analysis of heart rate variation (HRV) has become a popular non-invasive 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 non-stationary; 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. Fractal dimension (FD) of heart rate signals are calculated and compared with the wavelet analysis patterns. The FD obtained indicates more than 90% confidence interval for all the classes studied. © 2005 Elsevier SAS. All rights reserved.(Analysis of cardiac health using fractal dimension and wavelet transformation) Acharya, A.U.; Subbanna Bhat, P.; Kannathal, N.; Rao, A.; Lim, C.M.2005