Analysis of cardiac health using fractal dimension and wavelet transformation |
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Authors: | Rajendra Acharya U. P. Subbanna Bhat N. Kannathal Ashok Rao Choo Min Lim |
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Affiliation: | aDepartment of ECE, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599 489, Singapore;bDepartment of ECE, National Institute of Technology Karnataka, Surathkal, India;cCEDT, Indian Institute of Science, Bangalore, India |
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Abstract: | 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. |
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Keywords: | Heart rate Continuous wavelet transform Fractal dimension |
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