Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Acharya, A.U.Subject: search.filters.subject.Heart rate

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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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    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.