Matti Huotari, Antti Vehkaoja, Kari Määttä, Juha Röning (2013) Arterial pulse waves measured with EMFi and PPG sensors and comparison of the pulse waveform spectral and decomposition analysis in healthy subjects Finnish Journal of eHealth and eWelfare (FinJeHeW), vol. 5, no. 2-3, 2013, pp. 57-66. http://ojs.tsv.fi/index.php/stty/article/view/8173
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Arterial pulse waves measured with EMFi and PPG sensors and comparison of the pulse waveform spectral and decomposition analysis in healthy subjects Matti Huotari1, Antti Vehkaoja2, Kari Määttä1, Juha Röning1 1 Oulu University, Oulu, Finland, 2 Tampere University of Technology, Tampere, Finland Matti Huotari, Oulu University, Oulu, FINLAND. Email:
[email protected].
Abstract The purpose of this study is to show the time domain and frequency domain analysis of signals recorded with Elec‐ tromechanical Film (EMFi) and Photoplethysmographic (PPG) sensors in arterial elasticity estimation via pulse wave decomposition and spectral components obtained from left forefinger, wrist, and second toe arteries. ECG and pulse waves from the subjects were recorded from 7 persons (30‐60 y) in supine position. Decomposition of the pulse waves produces five components: percussion, tidal, dicrotic, repercussion, and retidal waves. Pulse wave decomposition parameters between EMFi and PPG are compared to detect variables for information on person’s arterial elasticity. Results show that elasticity information in the form of pulse wave decomposition from PPG and EMFi waves is obtainable and shows clear shortening between percussion wave and tidal wave peak time in PPG waveforms with age. The spectral information obtained with frequency domain analysis could also be valuable in assessment of the arterial elasticity. In addition, both PPG and EMFi measurements are absolutely non‐invasive and safe. In PPG measurement, the sensors are on the opposite sides of the finger tip, however, EMFi measure‐ ment needs the good skilled operator attaching the sensor on the patient’s wrist by touching gently to obtain accu‐ rate waveforms. Keywords: arterial elasticity measurement, electromechanical film (EMFi), photoplethysmography (PPG), pulse wave decomposition
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Introduction Arterial pulse wave is defined as a heart‐beat driven wave of blood that propagates via each artery into vein through capillaries. Various kind of medical information can be obtained by the detection of pulse wave and its detailed analysis. This information is not obtainable from the blood pressure or electrocardiographic measure‐ ments. It could be possible to make early diagnosis on the basis of the pulse wave analysis. Especially atherosclero‐ sis is the main cause of circulatory diseases. Its quantitative assessment is essential for making an early diagnosis of such diseases. In addition, persons with other cardiovascular diseases (CVD) may have decreased arterial elasticity compared with those free of CVDs. Change of arterial elasticity is one of the early markers of accelerated arterial aging and can correlate with many coronary risk factors. Especially arterial elasticity reflects the arterial and aortic expanding during left ventricular contraction. Arterial elasticity can be measured indirectly provided the measure‐ ment method is relevant and accurate enough. In this study, biophysical function and structure of the arteries have been measured by photoplethysmographic (PPG) and electromechanical film (EMFi) sensors. For the measurement data, we applied pulse wave decomposition (PWD), which reflects clearly the elasticity of the aorta and its periph‐ eral arteries. The combined PPG & EMFi measurements can establish aortic and arterial elasticity based on PWD of the both signals during a heart cycle. We do not necessarily need distance measurement required for pulse wave velocity estimation, which can be rather inaccurate in the case of the arterial tree [1]. The EMFi and PPG technolo‐ gies require a few electromechanical or opto‐electronic components: a EMFi film sensor connected to an amplifier, and a light source to illuminate the tissue (e.g. finger), and a photodetector to measure the small variations in light absorbance amplified also by a transimpedance amplifier. The obtained pulse wave is the peripheral pulse that can be decomposed into five logarithmic normal components. Despite its simplicity, the origins of the different com‐ ponents of the PPG or EMFi signal are not fully understood [2]. We believe that these parallel pulse waves can provide valuable information about the circulatory system with patient‐friendly and safe means. In the elderly and in the stiffened arteries, the forward and reflected pulse waves travel faster, i.e., pulse wave velocity (PWV) is higher than in a young person’s arteries which are elastic. The arterial waves reflected from the periphery of the arterial tree, return earlier merging with the systolic part of the incident wave causing augmenta‐ tion of the workload of the heart. Favorable softness between coupling of the left ventricle and the arterial tree is thus progressively lost. This loss can be greatest in the aorta, and least in the upper limbs. The wave reflection of the pulse wave due to increase in PWV also increases with age, but can be largely prevented by physical activity and proper diet. The amplitude spectrum of the ECG, EMFi, and PPG are changing by so‐called integral pulse fre‐ quency modulation (IPFM) [3]. This modulation is caused by the autonomic control mechanisms of cardiac func‐ tions which are involved in short‐term fluctuations in the time interval between the consecutive heart cycles. The IPFM reflects cardiac function which is also detected in the periphery of the arterial tree. Healthy modulation in coupling of the left ventricle and the arterial tree is, however, progressively lost. In our study we measure EMFi, two PPGs, and ECG signals, and we apply Fast Fourier Transform on the signals in addition to logarithmic normal function decomposition of the EMFi and PPG pulse waveforms.
Methods In this study, the PPG sensors are based on LEDs and photodetector, EMFi sensor is based on a plastic EMFi film, and ECG sensors are standard electrodes. Changes in light absorption, in the pressure, or in electrical potentials are acting on each sensor generating a measurable voltage. The EMFi sensor acts as a sensitive pressure sensor and the PPG sensor as a sensitive absorbance sensor. Signals from ECG, PPG, and EMFi sensors were recorded with the 27.5.2013
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PC from 7 healthy persons (30‐60 y) using a data acquisition card with the sampling frequency of 500 Hz. ECG sig‐ nal was used as reference in detecting features from the PPG and EMFi related signals. Pulse wave series from EMFi, PPG finger and toe were processed by Origin software as follows. Firstly, the alternating baseline on each signal bottom (minimum) was searched and then subtracted. Secondly, the peak of each signal (maximum) was searched, and the average of the peak values was calculated. The signal was divided by the average to obtain the baseline removed and normalized waveforms which has amplitude from the zero to round about one. Processing continues a pulse by a pulse in the PWD.
Results An example pulse wave from each sensor is shown in Figure 1 on which analysis in time domain and FFT in fre‐ quency domain was performed. Each pulse wave component from EMFi, PPG finger, and toe were processed by the software. Decomposed pulse waves from the left wrist (EMFi), and from the left forefinger and the left second toe (PPG) are decomposed in time domain and transformed in frequency domain. In Figure 1 it is shown EMFi (solid), PPG finger (dash dot), PPG toe (dashed) pulse waves, and electrocardiogram (ECG) (dot). They have the start time as zero and then decomposed with their residual and confidence intervals, respectively, in the Figure 2.
B EMFi_TTSS_30 D PPG2 (toe) F PPG1 (finger) H ECG
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Figure 1. The baseline removed normalized pulse waves: EMFi (left wrist, solid), PPG1 (left forefinger, dash dot), PPG2 (left second toe, dashed), and ECG (dot) (Male 30).
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Data: Data24_B TTSS_30-EMFI-2 Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting
a
Chi^2/DoF = 0.00007 R^2 = 0.99899
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Data: Data24_B TTSS_30-EMFI-2 Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting Chi^2/DoF = 0.00007 R^2 = 0.99899
0 ±0 0.16787 ±0.00041 0.57342 ±0.00164 0.19773 ±0.00031 0.29496 ±0.00254 0.11755 ±0.00795 0.0053 ±0.00027 0.47814 ±0.00355 0.17313 ±0.00546 0.07034 ±0.00109 0.6713 ±0.00765 0.12885 ±0.02126 0.01904 ±0.00192 0.806 ±0.00518 0.06443 ±0.01196 0.00423 ±0.00153
y0 xc1 w1 A1 xc2 w2 A2 xc3 w3 A3 xc4 w4 A4 xc5 w5 A5
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0,0 0,176 s
0,6 0,160 s_0,032 s_2,250 0,04 0,03 0,02 0,01 0,00 -0,01 -0,02 -0,03
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0,25 0,160 s_0,032 s_2,250 0,288
0,464 0,176 s 0,192 s (0,032 s)/2=0,016 s
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0,128 s+0,016 s= 0,144 s (0,032 s)/2=0,016 s
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0,128 s+0,016 s= 0,144 s
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0 ±0 0.16787 ±0.00041 0.57342 ±0.00164 0.19773 ±0.00031 0.29496 ±0.00254 0.11755 ±0.00795 0.0053 ±0.00027 0.47814 ±0.00355 0.17313 ±0.00546 0.07034 ±0.00109 0.6713 ±0.00765 0.12885 ±0.02126 0.01904 ±0.00192 0.806 ±0.00518 0.06443 ±0.01196 0.00423 ±0.00153
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Data: Data34_B TTSS_30PPG1_2 Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting
b
Chi^2/DoF = 0.00023 R^2 = 0.99707 y0 xc1 w1 A1 xc2 w2 A2 xc3 w3 A3 xc4 w4 A4 xc5 w5 A5
0,14217
0,4739 0,20536 s 0,26854
0 ±0 0.26002 ±0.00431 0.79597 ±0.00635 0.34821 ±0.00451 0.27935 ±0.00414 0.2149 ±0 0.02029 ±0.00115 0.4971 ±0.0093 0.21556 ±0.01328 0.11562 ±0.00534 0.6575 ±0.01008 0.11953 ±0.02158 0.03082 ±0.0073 0.77568 ±0.00501 0.06467 ±0.00868 0.01035 ±0.00307
0,17376 s
0,0158 s * 2=0,0316 s
0,64766 0,77403
0,12637 s_-0,0158 s_1,88887 0,12637 s
EMFip-PPG1P=0,14217 s - 0,128 s=-0,01417 s
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Figure 2a) A single EMFi pulse wave (solid, measured) which is here decomposed into components: percussion (dash), tidal (dot), dicrotic (dash dot), repercussion (short dash), and retidal wave (short dot). (Insert the two last wave components). The confidence interval (99%) is marketed short dot dot. The residual curve is shown in the lower panel. b) A single PPG1 (finger) pulse wave decomposed, respectively. (Male 30). 27.5.2013
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Data: Data30_B Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting
a
Chi^2/DoF = 0.00005 R^2 = 0.99943
0,14478
y0 xc1 w1 A1 xc2 w2 A2 xc3 w3 A3 xc4 w4 A4 xc5 w5 A5
0,29356
0 ±0 0.21327 ±0.00091 0.61405 ±0.00213 0.26783 ±0.00078 0.29341 ±0.00055 0.14128 ±0.00202 0.02649 ±0.00038 0.49686 ±0.00153 0.19327 ±0.00264 0.0898 ±0.00087 0.80032 ±0.00409 0.20676 ±0.00389 0.0672 ±0.00069 1.1614 ±0.00319 0.01339 ±0.00291 0.00052 ±0.0001
0,48485 0,19129
0,2763 0,76115
0,14878 s_0,004 s_2,028
1,16498
0,40383
PPG1p-EMFip=0,18637-0.14478=0,04159
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Data: Data2_B PL_60 Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting Chi^2/DoF = 0.00017 R^2 = 0.9982
0,18241
y0 xc1 w1 A1 xc2 w2 A2 xc3 w3 A3 xc4 w4 A4 xc5 w5 A5
0,5138 0,22783
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0 ±0 0.28169 ±0.00702 0.6794 ±0.00901 0.30214 ±0.00715 0.29168 ±0.00323 0.20137 ±0 0.03543 ±0.00244 0.55412 ±0.0201 0.29211 ±0.02518 0.2456 ±0.01681 0.80841 ±0.03208 0.17232 ±0.03695 0.09407 ±0.03278 0.98826 ±0.00982 0.10954 ±0.01568 0.0349 ±0.02001
0,78305 0,96946 0,18641
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t [s] Figure 3a) A EMFi pulse wave (solid, measured) which is decomposed into components: percussion (dash), tidal (dot), dicrotic (dash dot), repercussion (short dash), and retidal wave (short dot). The confidence interval (99%) is marketed short dot dot. The residual curve in the lower panel. b) A single PPG1 (finger) pulse wave decomposed, respectively. (Male 60). 27.5.2013
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0,010
Integral of Data6_C EMFi Integral of Data16_C PPGfinger Integral of Data27_C Integral of Data37_C Integral of Data48_C Integral of Data58_C
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Figure 4. Integral of residuals (EMFi solid) and PPG1 (finger, dash). Insert: Absolute value of residual of EMFi wave‐ form, (Male 30).
PPG2rel 1,1 1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0,0 -0,1 -0,1
Data: Data15_B TTSS_30_PPG2 Model: LogNormal Equation: y = y0 + A/(sqrt(2*PI)*w*x)*exp(-(ln(x/xc))^2/(2*w^2)) Weighting: y No weighting Chi^2/DoF = 0.0001 R^2 = 0.99891 y0 xc1 w1 A1 xc2 w2 A2 xc3 w3 A3
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0 ±0 0.22511 0.56858 0.25135 0.23982 0.20243 0.01611 0.60279 0.20447 0.03438
±0.00103 ±0.00198 ±0.00199 ±0.00162 ±0.00819 ±0.00075 ±0.00223 ±0.00478 ±0.00058
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t [s] Figure 5. The whole single PPG2 pulse wave (black, measured) which is here decomposed into components respec‐ tively as in Figure 2, but here only three components are found. (Male 30, Fig. 1). 27.5.2013
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FFT1_r[4149]: X = 3.174, Y = 0.0400
FFT2_r[4128]: X = 1,892; Y = 0,0238
FFT2_r[4152]: X = 3,357; Y = 0,0379
FFT2_r[4149]: X = 3,174; Y = 0,0505
FFT2_r[4146]: X = 2,991; Y = 0,0328
FFT2_r[4135]: X = 2,319; Y = 0,0357
FFT2_r[4132]: X = 2,136; Y = 0,0725
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FFT1_r[4152]: X = 3.357, Y = 0.0294
Frequency (Hz) FFT2_r[4117]: X = 1.2207, Y = 0.026
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FFT1_r[4146]: X = 2.991, Y = 0.0260
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FFT1_r[4128]: X = 1.892, Y = 0.0233
0,1 FFT2_r[4114]: X = 1.038, Y = 0.1178
FFT2_r[4111]: X = 0.8545 Y = 0.0271
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FFT1_r[4117]: X = 1.221, Y = 0.0303
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Frequency (Hz)
FFT1_r[4114]: X = 1.0376, Y = 0.1546
Angle(deg) 0,1
FFT1_r[4111]: X = 0.854, Y = 0.0388
FFT1_r[4101]: X = 0.244, Y = 0.0254
Amplitude
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Figure 6. The pulse wave of EMFi’s amplitude spectrum (up), and of PPG1’s amplitude spectrum (down) with the IPFM parameter values for the three first components for 20 s record. In the PPG1’s amplitude spectrum contains the breath rate frequency value (Male 30).
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FFT2_r[8289]: X = 2,9296875; Y = 0,0274294923
0,1 FFT1_r[8290]: X = 2,96020508; Y = 0,0426239233
FFT1_r[8265]: X = 2,197; Y = 0,0088
FFT1_r[8257]: X = 1,953125; Y = 0,074377257
FFT1_r[8250]: X = 1,740; Y = 0,0092
FFT1_r[8233]: X = 1,221; Y = 0,0074
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FFT2_r[8265]: X = 2,197; Y = 0,0050
0,025 FFT1_r[8225]: X = 0,977; Y = 0,1695
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FFT2_r[8257]: X = 1,953125; Y = 0,0580455913
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FFT1_r[8217]: X = 0,732; Y = 0,0142
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FFT2_r[8233]: X = 1,221; Y = 0,0054
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FFT2_r[8225]: X = 0,977; Y = 0,1722
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FFT2_r[8217]: X = 0,732; Y = 0,0181
FFT2_r[8201]: X = 0,244; Y = 0,0171
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Figure 7. The EMFi pulse wave signal’s amplitude spectrum (up), and of PPG1’s amplitude spectrum (down) with the IPFM parameter values for the three first components. In the PPG1’s and EMFi’s amplitude spectrum contain also the breath rate frequency value for 20 s signal record as a sample length (Male 31).
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FFT3_r[8274]: X = 2,472; Y = 0,0410
FFT3_r[8220]: X = 0,824; Y = 0,1213
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FFT3_r[8247]: X = 1,648; Y = 0,0562
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0,125 FFT5_r[8220]: X = 0,824; Y = 0,1535
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FFT5_r[8274]: X = 2,472; Y = 0,0298
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Figure 8. The EMFi pulse wave signal’s amplitude spectrum (up), and of PPG1’s amplitude spectrum (down). In the PPG1’s and EMFi’s amplitude spectrum contain also the breath rate frequency value (Male 60). 27.5.2013
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The comparison of the PPG1 pulse waves shows that the tidal wave comes closer to the percussion wave peak value when person’s age increases becoming shorter than the percussion wave defined from the start to the wave maximum (Fig. 2b and 3b). The integrals of residual errors for each sensor and in different measurement show good relation. At the first, the integral of each residual overlaps and then EMFi and PPG based residuals differ after the systolic phase (Figure 4). In Figure 5 it is shown a typical toe PPG pulse wave which contains at least three components according to the decomposition. The comparison of both the EMFi’s and PPG1’s amplitude spectra shows that the modulation frequency disappears or comes close to the carrier frequency as the person’s age in‐ creases (Fig. 6, 7, and 8). This study shows also that both the EMFi and PPG pulse waveforms can be decomposed to their component waves, namely, percussion wave, tidal wave, dicrotic wave, repercussion, and retidal waves. Also in frequency domain the amplitude spectra of the respective pulse waves contain at least five or six frequency components.
Discussion Clinical research is necessary to quantify the ageing effects in relation to the obtained variables and also to explore pulse waveform changes with subject age. However, based on these optical and mechanical measurement meth‐ ods it was shown the changes in the pulse waveform. Measurement reliability and repeatability is very good pro‐ vided the EMFi measurements are done by a skilled operator. Pulse waveform analysis of both the PPG and EMFi offers an alternative means of non‐invasive cardiovascular monitoring, but further both software and hardware development is required to enable user‐friendly clinical and preclinical measurement and analysis system. In PPG, the sensors are on the opposite sides of the finger tip, however, EMFi needs the good skilled operator (A.V.) at‐ taching the sensor on the patient’s wrist by touching gently to obtain waveforms. The elasticity is obtained quanti‐ tatively from both EMFi and PPG pulse signals both in time and frequency domain because of the components time interval changes or the spectral changes clearly inspected. However, from time domain, decomposition into loga‐ rithmic normal function, differs from that obtained from frequency domain because the latter contains many pulse waves which are IPFM modulated and they frequency components overlap. This information from the pulse wave propagation analysis can’t be received. In studies, EMFi and PPG theory should be described when analyzing arte‐ rial pulse wave signals because they contain effects of respiration, autonomous nervous activity, gastric mobility, and also arterial properties, i.e., arterial diseases.
References [1] Alametsä J, Palomäki A. Comparison of local pulse wave velocity values acquired with EMFi sensor. Finnish Journal of eHealth and eWelfare 2012;4(2):89‐98. [2] Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Meas‐ urement 2007;28:R1–R39. [3] Middleton PM et al. Spectral analysis of finger photoplethysmograpic waveform variability in a model of mild to moderate haemorrhage. Journal of Clinical Monitoring and Computing 2008;22:343‐353.
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