Abstract
This paper introduces a new method for R wave's locations using the multiscale wavelet analysis, that is based on Mallat's and Hwang's approach for singularity detection via local maxima of the wavelet coefficients signals. Using a first derivative Gaussian function as prototype wavelet, we apply the pointwise product of the wavelet coefficients (PWCs) over some successive scales, in order to enhance the peak amplitude of the modulus maxima line and to reduce noise. The R wave corresponds to two modulus maximum lines with opposite signs (min-max) of multi-scale product. The proposed algorithm does not include regularity analysis but only amplitude-based criteria. We evaluated the algorithm on two manually annotated databases, such as MIT-BIH Arrhythmia and QT.
Highlights
QRS complex detectors are extremely useful tools for the analysis of ECG signals
The majority of them are based on singularity detection via local maxima of the wavelet coefficients signal; therein the correspondence between singularities of a function and local maxima in its wavelet transform is investigated
The algorithm presented by Josko [4] is based on Discrete Wavelet Transform (DWT), computed at selected characteristic scales, where QRS complex spectrum energy is the largest
Summary
QRS complex detectors are extremely useful tools for the analysis of ECG signals. They are used for finding the fiducial points for averaging methods and to calculate the RR time series in Heart Rate Variability techniques. The algorithm presented by Josko [4] is based on Discrete Wavelet Transform (DWT), computed at selected characteristic scales, where QRS complex spectrum energy is the largest. For each transient present in the input signal coefficients of DWT produce localized extremes at several consecutive scales This property is used in detection process. Zhang and Yong [5] present a novel algorithm based on continuous wavelet transform (CWT) to accurately detect QRS It employs a first-order derivativebased differentiator to suppress noise and baseline drift and uses high-scale continuous wavelet transform to peak the zero crossing R point produced by differentiator to ease the task of QRS detection. We present here a new method for R wave’s locations using the multiscale wavelet analysis, that is based on Mallat’s and Hwang’s approach for singularity detection via local maxima of the wavelet coefficients signals.
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