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

Lee, Yang-Han

Tseng,Hsien-Wei

English

Tamkang University Institutional Repository

 Abstract--Heartbeat information is a crucial indicator in the clinical detection of life parameters. Current mainstream methods used for detecting heartbeats are contact detection technologies. Noncontact detection refers to using external energy to detect human physiological environment changes without contacting the human body.  I. INTRODUCTION Heartbeat information is a crucial indicator in the clinical detection of life parameters. Current mainstream methods used for detecting heartbeats are contact detection technologies, such as electrocardiography (ECG) [1] and photoplethysmography (PPG) [2]. Thus far, ECG has been a suitable indicator for evaluating physical health status. The major advantage of the contact detection methods is noninvasive measurements and complete waveforms, which can provide clinical references for physicians. However,measuring using ECG requires applying electrically conductive gel as a medium to attach conductive electrode pads onto the chest of the subject, rendering a low level of convenience for the user. The PPG refers to the signal of blood pressure changes sensed using optical sensing. The measurement method involves emitting lights on a selected skin area. The light enters the skin and is absorbed by blood, generating continual variations. The graph depicted according to the continual variation of light is the PPG. The advantage of PPG is that little electrical noise is produced. Although PPG requires measurement devices to be worn, it is more convenient for users, compared with ECG .  II. EXPERIMENTAL EQUIPMENT The hardware part used in this study involved employing a video camera embedded in basic laptop computers for filming human faces. The video color was 24-bit RGB, at a 640 X 480 resolution and 30 frames per second (FPS). The software part involved using the LabVIEW platform, developed by National Instruments, combined with IMAQ Vision Function Palettes. The control group in the test used ECG devices. During experimental measurement, the participants wore the measurement device to facilitate comparison with the experimental data in this study. Fig.1 shows the state during measurement[3].  This paper is supported by Ningde Normal University Campus Project(Project No.: 2014Q45) Corresponding Author:H-W Tseng(email:hsienwei.tseng@gmail.com)     Fig.1. Measurement state diagram  III. EXPERIMENTAL PROCEDURE Fig.2 shows the Experimental Procedure. After the image was photographed, image preprocessing was conducted, including color channel separation, selection of region of interest (ROI), and calculation of color histogram means. The color histogram means of the three channels measured during image preprocessing were processed using ICA. The values of the three independent components were determined. Subsequently, fast Fourier analysis was used for determining the frequency spectrum of the color histogram means in the green channel and the data of three independent components. Within a frequency range between 0.75 and 4 Hz (heartbeat: 45–240 beats per minute; BPM), the strongest intensity in the frequency spectrum and the corresponding frequencies were determined. The highest independent component value among the frequency spectra of the three independent components was selected. Multiplying the frequency of the selected independent component by 60 determined the required heartbeat value (BPM). The frequency spectrum of the color histogram of the green channel directly converted the frequency at the maximal energy into heartbeat value (BPM). Finally, the two individual heartbeat values were compared with the heartbeat values measured using ECG devices.   Analysis of Noncontact Heartbeat Detection Yang-Han Lee1, Hsien-Wei Tseng2*, Chien-Da Huang1, Yung-Wen Lee1, and Chin-Shan Hung3 1Department of Electronic Engineering, Tamkang University, New Taipei, Taiwan 2Department of Computer Science, Ningde Normal University, Ningde, Fujian, China 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 108  Original imageColor channel: Green channelColor channel: Red channelColor channel: Blue channelSet ROI and calculate the mean of color histogram within the sampled areaMean of greenhistogramMean of red histogramMean of bluehistogramICAFast Fourier analysis, selection of the segment between 0.75 and 4 Hz, and identification of the frequency with highest energyHeartbeat value determined (Green trace)Independent component 2Independent component 1Independent component 3Fast Fourier analysis, selection of the segment between 0.75 and 4 Hz, and calculation of the strongest intensity and corresponding frequencyDetermine the component with the strongest intensity value among the three componentsHeartbeat value determined (ICA)0 ~ nt t0( ) ~ ( )r r nx t x t 0( ) ~ ( )g g nx t x t 0( ) ~ ( )b b nx t x t1 0 1( ) ~ ( )nc t c t 2 0 2( ) ~ ( )nc t c t 3 0 3( ) ~ ( )nc t c tImage preprocessingHeartbeat detection Fig.2. Experimental procedure IV. EXPERIMENTAL RESULTS This experiment was conducted in an indoor space. During the experiment, each participant wore an ECG measurement device to facilitate a comparison with the data generated in this study.  Table I shows that when the measurement time was 5 s, the error was the largest. The error rates for 10 s and 20 s were within acceptable ranges. V. CONCLUSION This study proposes a set of image-based noncontact heartbeat measurement methods that involve using varying human face absorptions of light to detect heartbeats. By performing image processing and ICA, the hidden information in images were identified and concentrated. Subsequently, the frequency domain transformation and heartbeat estimation processes were used for achieving noncontact automatic measurement. By applying Cases and after a total of 80 iterations of measurements, using ECG measurement values as the indicators, a differential value percentage higher than 5% was identified as an error measurement [4]. The results are presented in Table II.  Table II indicates that, regarding the measurement error rates, the ICA processing reduced error rates by 19%, improving the original values measured using green light variations only. In the cases of this study, the results clearly indicate that skin color and heartbeat rates do not affect measurements. Moreover, at a video camera sampling rate of 30 FPS, one iteration of heartbeat measurement can be completed in 10 s. ACKNOWLEDGMENT This study is assisted by Mr. Jhe-Yuan Lin, thanks for his contribution. References [1] K.-L. Hsieh, “An Implementation of Individual ECG Acquisition System,” Department of Electrical Engineering, Tamkang University, 2006. [2] Y.-H. Chen, “A Study on the Correlation among, Heart Rate, PPG, and Non-invasive Pulse Signals,” Department of Automatic Control Engineering, Feng Chia University, 2005. [3] T. Pursche, J. Krajewski and R. Moeller, “Video-based heart rate measurement from human faces,” 2012 IEEE International Conference On Consumer Electronics(ICCE), Jan. 2012, pp. 544-545. [4] Chun-Li Wang, Wei-Jan Chen, Wan-Jing Ho, Lung-An Hsu, Nye-Jan Cheng, Tsu-Shiu Hsu and Chi-Tai Kuo, “Assessment of Doppler-derived Aortic Flow in Atrial Fibrillation Using Beats with Equal Subsequent Cycles,” Acta Cardiologica Sinica, vol. 22, no. 1, Mar. 2006, pp. 16-23. TABLE I DIFFERENTIAL VALUES AND DIFFERENTIAL VALUE STANDARD DEVIATIONS Item      Time Green Raw Trace V.S ECG Maximal Differential Value (BPM) Green Raw Trace V.S ECG DifferentialValue Standard Deviation (BPM) ICA V.S ECG Maximal Differential Value (BPM) ICA V.S ECG DifferentialValue Standard Deviation (BPM) 20 s 7.21 2.82 5.35 1.53 10 s 28.52 8.82 5.85 1.56 5 s 38.31 11.87 52.98 15.35TABLE II MEASUREMENT OF ERROR RATES Item   Method Number of items with a percentage of differential values higher than 5%   Error rate (%) Green trace 33 41.25 ICA 18 22.5 109

Analysis of Noncontact Heartbeat Detection

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Analysis of Noncontact Heartbeat Detection

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