Multiresolution wavelet‐based QRS complex detection algorithm suited to several abnormal morphologies

Abstract

The electrocardiogram (ECG) signal is considered as one of the most important tools in clinical practice in order to assess the cardiac status of patients. In this study, an improved QRS (Q wave, R wave, S wave) complex detection algorithm is proposed based on the multiresolution wavelet analysis. In the first step, high frequency noise and baseline wander can be distinguished from ECG data based on their specific frequency contents. Hence, removing corresponding detail coefficients leads to enhance the performance of the detection algorithm. After this, the author's method is based on the power spectrum of decomposition signals for selecting detail coefficient corresponding to the frequency band of the QRS complex. Hence, the authors have proposed a function g as the combination of the selected detail coefficients using two parameters λ 1 and λ 2 , which correspond to the proportion of the frequency ranges of the selected detail compared with the frequency range of the QRS complex. The proposed algorithm is evaluated using the whole arrhythmia database. It presents considerable capability in cases of low signal-to-noise ratio, high baseline wander and abnormal morphologies. The results of evaluation show the good detection performance; they have obtained a global sensitivity of 99.87%, a positive predectivity of 99.79% and a percentage error of 0.34%.

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

“…Various pre-processing techniques have been proposed in the literature, such as the derivative-based technique used in the classic and popular algorithm proposed by Pan and Tompkins in [10]. Numerous other pre-processing techniques have also been proposed, including those based on artificial neural networks [11][12][13], wavelet transforms [14][15][16][17], quadratic filter [18], S-transform [19], sparse derivatives [20] and Shannon energy envelope [21,22].…”

Novel Real-Time FPGA-Based R-Wave Detection Using Lifting Wavelet

“…The accuracy and speed of R-wave detection affect the performance of P-ECG-MD directly. Up to now, lots of R-wave detection algorithms have been proposed, including those based on differential threshold [16,18,24], wavelet transform [2,3,11,13,14], nonlinear transform [10,25], template matching [6] and other techniques [4,12]. In [18], Pan and Tompkins designed a band-pass filter for denoising and used a dual threshold to detect R-wave.…”

“…However, these methods based on the time domain analysis have a disadvantage that their accuracy and anti-interference ability are not ideal. Since Li et al [11] successfully used the quadratic spline wavelet to identify the characteristic points of ECG, wavelet transform had been widely researched in ECG analysis [2,3,13,14]. Martinez et al [14] completed Li's algorithm by identifying the P-and T-wave.…”

“…The Daubechies wavelet functions had become popular in the ECG feature point extraction since Mahmoodabadi et al [13] pointed out that the wavelet transform could achieve a better detection performance if its scaling function was more similar to the shape of ECG. Researchers have developed selective coefficient methods based on Daubechies wavelet to improve the detection accuracy [2,3]. Although the traditional wavelet transform has a high accuracy rate for the QRS complex detection, its application in the embedded real-time system is still restricted because of its high computational complexity.…”

A Novel Weak Signal Detection Method of Electromagnetic LWD Based on a Duffing Oscillator

“…Frequency-domain methods are based on the signal power spectrum analysis [12] but are more suitable for weak periodic signal detections. Other weak signal detection methods deploy wavelet analysis [13,14], high-order spectrum analysis [15,16], Hilbert-Huang transform [17], and artificial neural network [18]. However, in the wavelet analysis, the selection of the wavelet base function and the scale range are very complex and lack the general method.…”