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.

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dc.contributor.authorAcharya, A.U.
dc.contributor.authorSubbanna Bhat, P.
dc.contributor.authorKannathal, N.
dc.contributor.authorRao, A.
dc.contributor.authorLim, C.M.
dc.date.accessioned2026-02-05T11:00:18Z
dc.date.issuedAnalysis of cardiac health using fractal dimension and wavelet transformation
dc.description.abstract2005
dc.identifier.citationITBM-RBM, 2005, 26, 2, pp. 133-139
dc.identifier.issn12979562
dc.identifier.urihttps://doi.org/10.1016/j.rbmret.2005.02.001
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/27924
dc.subjectarticle
dc.subjectbradycardia
dc.subjectcalculation
dc.subjectcomplete heart block
dc.subjectconfidence interval
dc.subjectcongestive cardiomyopathy
dc.subjectfractal analysis
dc.subjecthealth
dc.subjectheart atrium fibrillation
dc.subjectheart rate
dc.subjectischemic heart disease
dc.subjectsick sinus syndrome
dc.subjectsignal detection
dc.subjectspectral sensitivity
dc.subjectsupraventricular premature beat
dc.subjecttime
dc.titleAnalysis 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.

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