# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 1 Introduction
[PERSON] and [PERSON] [5] developed an R-peak recognition method for wearable single-lead ECG devices. An ECG signal transformation was proposed to enhance QRS segments and restrain P and T waves. The QRS fiducial point is determined based on the detected crests and troughs of the transformed signal, and the R point can be recognized based on four QRS waveform templates. Moreover, several studies have proposed wavelet-based methods for the detection of QRS peaks. [PERSON] et al. [12] proposed an R-peaks detection method based on a stationary wavelet transform (SWT). Their algorithm used the information of local maxima, minima, and zero crossings of the fourth level detail coefficients to identify the significant points for detection and delineation of the QRS complexes, as well as the detection and identification of the QRS individual wave peaks. [PERSON] et al. [14] introduced a QRS complex detector based on a biorthogonal spline wavelet transform for wearable healthcare devices. The wavelet detail coefficients of the third and fourth levels are multiplied using the Booth multiplier, and a soft threshold method is applied to get the QRS peaks by the QRS peak detector block. The method proposed by [PERSON] et al. [18] used the product of the wavelet detail coefficients of the fourth and fifth levels to localize QRS complexes after a preliminary investigation of QRS spectral parameters. [PERSON] et al. [19] developed a method based on the power spectrum of the combination of the wavelet detail coefficients at the fourth and fifth levels. A moving window with specific width (160 ms) is used to investigate the positions of the maximum amplitude points in each window location, defining the QRS complex positions. All of the above-mentioned techniques have demonstrated their superiority in the detection of QRS peaks. However, if we consider the requirements of hardware complexity and detection accuracy together, wavelet-based approaches can play a trade-off role [14; 20]. Providing a noise-free ECG signal is important for the QRS peak decision. However, noise is usually broadband and overlaps with the QRS complex. It is not easy to accurately separate the noise from the ECG signal without destroying the QRS waveform. The pre-filtering out of the high-frequency components not only reduces the noise level but also causes a reduction in the amplitudes of the QRS peaks and thus reduces their the detection accuracy. This study therefore proposes a novel wavelet-based algorithm for the detection of QRS complex peaks. A four-level biorthogonal spline wavelet transform is introduced for the decomposition of ECG signals. The wavelet detail signals of the second, third, and fourth levels are employed in the proposed QRS peak detection algorithm. A noise evaluation method is proposed to quantify the amount of noise and to select a lower-noise signal between the third and fourth wavelet detail signals for detection of QRS peaks instead of pre-filtering out the high-frequency noise in the preprocessing stage. The rest of this paper is organized as follows. Section 2 describes the ECG recordings obtained from the MIT-BIH arrhythmia database. Section 3 presents the proposed wavelet-based algorithm for detection of the QRS complex peaks. Results and discussion are given in Section 4. Finally, Section 5 concludes this study.