[[sponsorship]]IEEE[[conferencetype]]國際[[conferencedate]]20150606~20150608[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]台灣/台北 國立臺灣科技大

[[corresponding]]Lee,Yang-Han

Tseng, Hsien-Wei

English

IEEE國際20150606~20150608電子版Y台灣/台北 國立臺灣科技大

Lee,Yang-Han

Tamkang University Institutional Repository

Abstract—The heart is the motor of the human body, and the cessation of heartbeat signifies the end of life. Numerous cardiac pathologies such as arrhythmia, myocardial infarction, and cardiac hypertrophy occur. Electrocardiography (ECG) devices are typically required to examine the heart in hospitals. Physicians can use subtle differences in ECG signals to determine whether a patient has a heart disease. Thus, this study designed an ambulatory ECG circuit and applied software to determine the characteristic points of the QRS complex. I. INTRODUCTION The QRS complex is the most distinct period among electrocardiogram (ECG) signals. A simple hardware device and improved ECG lead measurements were developed in this study. In addition, software was employed to detect the characteristic points of the QRS complex and record ambulatory ECG signals. II. RESEARCH BACKGROUND A. Electrocardiogram Waveform Composition When the heart contracts, weak electrical signals can be detected on the body surface. The changes in potential of the heart and recorded, forming an ECG graph. A complete ECG cycle consists of the P,Q,R,S, and T waves, as shown in Fig. 1.  Fig. 1. PQRST waves in an ECG. B. Standard Placement of Limb Leads The standard placement of limb leads is shown in Fig. 2. This method places the leads on three axes, which form a triangle among the probe locations, with the heart in the middle of the triangle.  This paper is supported by Ningde Normal University Research Project(Project No.: 2014Y007) Corresponding author: Y. H. Lee(e-mail: yhleepp@gmail.com)   Fig. 2 Standard placement of limb leads III. EXPERIMENTAL PROCEDURE A. Wavelet Theory Previously, nearly all standard tools for signal analysis applied Fourier transforms [1]–[2] to extract data from signals, as shown in (1). ∫∞∞−−= dtetfF jwt)()(ω             (1) B. Determining the R Wave By Using the So and Chan Algorithm To capture the QRS complex, the position of the R wave must first be obtained. The So and Chan algorithm was adopted in this study. The process is shown in Fig. 3.  Fig. 3. Flowchart of the So and Chan algorithm.   Detection and Analysis of the Designed Circuit for Ambulatory ECG Electrical  Characteristic Points Hsien-Wei Tseng1, Yang-Han Lee2*, Chien-Da Huang2, and Hsiao-Yu Huang 3 1Department of Computer Science, Ningde Normal University, Ningde, Fujian, China 2Department of Electronic Engineering, Tamkang University, New Taipei, Taiwan 3 Department of  Electronic Engineering, St. John's University, New Taipei, Taiwan 2015 International Conference on Consumer Electronics-Taiwan (ICCE-TW)978-1-4799-8745-0/15/$31.00©2015 IEEE 106  IV. HARDWARE DESIGN A. Improved Hardware Architecture This study focused on ambulatory ECG. The primary problem to be resolved in ambulatory ECG is signal noise processing. During exercise, the body produces noise, including electromechanical signals. To reduce costs and to detect the true characteristic points of the QRS complex, two leads, instead of three leads, were used for detection. This circuit diagram is shown in Fig. 4. All of the detected signals were exported to MATLAB [3]–[4], and the characteristic points of the QRS complex were processed using an algorithm in the software.  Fig. 4. The dual lead ECG circuit diagram.  In this circuit, AD620 [5] was used as the instrumentation amplifier. Because only two leads, namely input+ and input-, were used, the voltage was unstable, causing the ECG baseline to drift and exhibit fluctuating levels. Thus, an OP07 amplifier was added to regulate the signals. The result is shown in Fig. 5. B. The final product for dual lead measurement  Fig. 5 The final product for dual lead  measurement V. COMPARISON OF SIMULATED RESULTS  A. Simulated ECG Using So and Chan Processing Fig. 6 shows the R wave obtained using a third order transform and So and Chan algorithm processing of the wavelet. Each calculated R wave is labelled.  Fig. 6. Diagram of the R points. B. Analyzing Effectiveness By Using the Physionet Database  The Physionet [6] Web site combines the MIT-BIH Arrhythmia Database developed by the Massachusetts Institute of Technology and other databases developed by schools such as Boston Medical Center of Boston University. Physionet consists of abundant reference data, including ECG signal data, ECG waveforms, and composite data. Table 1 shows that the average margin of error in the calculations in this study was approximately 3.478 ms. According to the CSE , the standard margin of error is 6.6 ms for QRS-on and 9.2 ms for QRS-off. The largest margin of error is approximately 15.8 ms. Thus, the effectiveness of the results of this study is acceptable. TABLE I  COMPARISON OF MARGINS OF ERROR Record No. QRS Interval (ms) (QT Database) QRS Interval (ms) (Present study) Average Margin of Error (ms) sel102 108.6 114 -5.4 sel103 67.4432 67.2 0.2432 sel104 116.52 119.2 -2.68 sel114 54.8 69.2 -14.4 sel116 60.4 58 2.4 sel117 89.2 92.4 -3.2 sel14046 70.8 61.6 9.2 sel15814 127.72 125.2 2.52 sel16265 67.2 56 11.2 sel16272 47.8 32.8 15  QRS Interval (ms)  (QT Database) QRS Interval (ms) (present study) Average Margin of Error (ms) Average 70.68707556 67.20888889 3.478186667  VI. CONCLUSION The Physionet database was used in this study to verify that the proposed algorithm can detect the QRS complex and ambulatory ECG signals. The article entitled “Delineation of QRS-complex, P and T-wave in 12-lead ECG,” published in the IJCSNS, reported an average margin of error of 8.4 ms. In comparison, the margin of error was 3.478 ms in the current study, implying that the proposed device is more effective. REFERENCE [1] Soo-Chang Pei, Min-Hung Yeh, Tzyy-Liang Luo, “Fractional Fourier series expansion for finite signals and dual extension to discrete-time fractional Fourier transform, ” IEEE Transactions on Signal Processing, vol. 47, 1999, pp. 2883-2888 [2] John G.Proakis Masoud Salehi, Communication Systems Enginnering, Prentice Hall, 2002 [3] Pratap , Rudra, Getting started with MATLAB 6 : a quick introduction for scientists and engineers, 2002 [4] Marchand, Patrick., Graphics and GUIs with MATLAB, 2003 [5] http://www.stanford.edu/class/ee122/Parts_Info/datasheets/ad620.pdf [6] http://www.physionet.org/cgi-bin/atm/ATM  107

Detection and Analysis of the D esigned Circuit for Ambulatory ECG Electrical Characteristic Points

http://tkuir.lib.tku.edu.tw:8080/dspace/bitstream/987654321/103574/2/p106-tseng.pdf

Detection and Analysis of the D esigned Circuit for Ambulatory ECG Electrical Characteristic Points

Abstract

Similar works

Full text

Available Versions

Tamkang University Institutional Repository