BioMed Research International

Volume 2017 (2017), Article ID 5168346, 12 pages

https://doi.org/10.1155/2017/5168346

## A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform

^{1}School of Information Science and Engineering, Central South University, Changsha, Hunan Province 410083, China^{2}Hunan Vocational College of Commerce, Changsha, Hunan Province 410205, China^{3}School of Computer Science and Educational Software, Guangzhou University, Guangzhou, Guangdong Province 510006, China

Correspondence should be addressed to Guojun Wang

Received 16 December 2016; Revised 18 February 2017; Accepted 2 April 2017; Published 17 May 2017

Academic Editor: Volker Rasche

Copyright © 2017 Ziran Peng and Guojun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential. The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.

#### 1. Introduction

ECG can record the physiological states of the heart and cardiovascular system in a real-time manner, and thus it is widely used for the detection and diagnosis of clinical heart disease [1]. ECG eigenvalue automatic detection can rapidly and accurately detect heart diseases [2]. Currently, ECG eigenvalue detection is based on multiple algorithms: the envelope analysis technique can effectively decompose complex signals into single component signals, which are typically empirical mode decomposition (EMD) and local mean decomposition (LMD). EMD is an adaptive signal decomposition method, the data from high frequency to low frequency decomposition into a series of intrinsic mode function (IMF) and a margin. Lahmiri and Boukadoum proposed A Weighted Bio-Signal Denoising Approach Using EMD in [3], which shows some advantages in ECG denoising. LMD solves the problem of endpoint effect of EMD method to a certain extent. However, both LMD and EMD belong to the recursive model, which have the problems of modal aliasing [4], end effect, being sensitive to noise and sampling, and difficulty in separating similar frequency components. But there is a problem caused by EMD [5]: in the background of bad noise, IMF will be submerged in the background of noise that leads to missing the signal characteristic component. Variational mode decomposition (VMD) solved this problem by transforming modal estimates into variational problems [6, 7].

The above methods are suitable for analyzing and dealing with aperiodic mutational signals [8]. If the periodic signals such as ECG are used to calculate the amount of periodic signals, it is difficult to determine threshold problems, especially for mobile real-time ECG monitoring, requiring low computational complexity and high detection accuracy, so the optimized wavelet processing is an ideal choice [9, 10].

However, two problems remain unresolved: firstly, which layer is more appropriate for feature detection after wavelet transform and secondly, whether the high-pass coefficient or low-pass coefficient is appropriate for feature location. If these key parameters are decided only by experiences, it is difficult to obtain systematic and scientific conclusions by experiments and emulations [11, 12]. This study investigated a detection method, which involved directly catching the signal frequency component during wavelet transform according to the frequency characteristics for different wavebands of ECG signal, to accurately locate the eigenvalue during wavelet transform. Currently, detection algorithms are mainly aimed at location and extraction of the QRS eigenvalue. Using these results and further reversing the electrophysiological activity of myocardial cells will be of great significance to automatic analysis and diagnosis of the physiological status of the heart [13]. Based on the eigenvalue detection, this research further studied the reverse analysis of myocardial action potential to enable automatic detection and diagnosis of heart diseases such as myocardial ischemia and heart failure.

#### 2. Specific Frequency Coefficient Obtained by Wavelet Bandpass Filtering

A wavelet transform was performed for signal with frequency , where the high-pass component frequency was and the low-pass component frequency was . The high-pass filter and low-pass filter have two intersections in : , . The two intersections represent the region where low frequency transitions to high frequency. According to the Fourier convolution theorem, it can be concluded that the role of and on signal is equivalent to the transfer function in filtering circuit analysis. For further analysis of the suppression multiple of signals at the two critical points, ; for , , so , obviously , and then . Clearly, it is a function related to with faster convergence rate. If , , and if , . For a given positive close to 0, there exists that always makes . Then an appropriate vanishing moment can make the suppression multiple at the critical point infinitely small and thus make the extra-regional gains of signal passing this point close to 0, theoretically equivalent to cut-off state. Assume the signal sampling frequency is , including the noise with frequency of . Assume the wave-trapped and denoising tolerable frequency bandwidth is , where is frequency bandwidth increment. If the signal section is , after each wavelet transform, the high-pass component covers the frequencies of , while the low-pass component covers the frequencies of . For higher orders of filter for wave trapping, the overlaying area of high-pass frequency and low-pass frequency is smaller, the filter frequency curve is steeper, and the energy is more concentrated. To facilitate calculation, this study adopted normalised frequency as the unit: for the normalised frequency in , the actual frequency refers to

Any frequency range , after normalised processing, can be expressed in solid area number field. According to the Shannon Theory, the sampling frequency should not be less than two times the maximum frequency in the analog signal frequency spectrum, so when directly filtering the sampling signal, the normalised frequency should be in , while for filtering at the layer of second or above, should be in .

Assume the normalised frequency for signal is in , and for the normalised frequency , if , the signal of frequency section can be extracted from by bandpass filtering after wavelet transform. As a demonstration, a wavelet transform is performed for signal , where refers to the low-pass component after transform and refers to the high-pass component after transform. According to the discussed situations, the following operations can be made according to concrete situations: indicates , where the result is returned, and then the algorithm ends. indicates and a wavelet transform is performed for signal . ; then this algorithm is repeated. indicates , and a wavelet transform is performed for signal . ; then this algorithm is repeated.

#### 3. Eigenvalue Extraction of QRS Wave Group and T Wave

How to accurately locate QRS wave group and T wave and extract their eigenvalues is of great significance for the detection of ECG eigenvalues. Affected by EMG interference, power frequency interference, and electromagnetic interference and noises, ECG signals are mixed with baseline drift and various noises, causing difficulties in the accurate location of ECG eigenvalues [14]. The basic method is to first analyze the frequency features of QRS wave group and extract the frequency components during wavelet decomposition, then enhance the signals according to certain strategy, and finally accurately locate the QRS wave group and T wave.

##### 3.1. Analysis of Frequency Features of QRS Wave and T Wave

Figure 1 shows the energy distribution of QRS wave and T wave on the frequency spectrum. It shows that the bandwidth for QRS wave is 0–40 Hz, accumulating nearly 99% of energy. To extract the wavelet system of QRS wave by bandpass filtering, the frequency bandwidth should be limited to about 20 Hz, so that the frequency section of 20 Hz bandwidth with maximum energy density in 0–40 Hz is achieved. By assuming only covers QRS wave signals, can transform from time domain to frequency domain. Section of 10 Hz bandwidth with maximum energy density is calculated by the following formulae: