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Heart Rate Variability Feature Extraction Using Continuous Wavelet Transform

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This paper provides a novel approach for extracting Heart Rate Variability (HRV) from an ECG signal. The ECG signal is filtered through a Multi-Resolution Array (MRA) filter bank of Wavelets and the instantaneous angular frequency of the heart is extracted using the phase change of the filtered signal. The modulation in the fundamental frequency of the heart, thus obtained, represents HRV and is a strong indicator of heart disease or impending abnormality of the cardiovascular system. The wavelet function used is a modified Gabor wavelet which is made self-adaptive to extract the instantaneous frequency of the heart. The driver function for the adaptive modified Gabor wavelet is derived using the Continuous Wavelet Transform (CWT). The technique proposed does not require re-sampling and is shown to be more accurate and compact as compared to that obtained using the traditional method of R peak detection. The method works well even in the presence of a noisy ECG signal since the wavelet transform is tuned to extract only the required frequency. The proposed method has been tested for spectral response of HRV for normal as well as abnormal samples of ECG signals chosen from the MIT data base.
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Keywords: CWT; HIF; HRV; MODIFIED GABOR FUNCTION; MRA

Document Type: Research Article

Publication date: September 1, 2015

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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