Cardiovasc Eng DOI 10.1007/s10558-009-9080-5
ORIGINAL PAPER
Pulse Wave Velocity and Digital Volume Pulse as Indirect Estimators of Blood Pressure: Pilot Study on Healthy Volunteers Juan M. Padilla Æ Enrique J. Berjano Æ Javier Sa´iz Æ Rafael Rodriguez Æ Lorenzo Fa´cila
Ó Springer Science+Business Media, LLC 2009
Abstract The purpose of the study was to asses the potential use of pulse wave velocity (PWV) and digital volume pulse (DVP) as estimators of systolic (SBP) and diastolic (DPB) blood pressure. Single and multiple correlation studies were conducted, including biometric parameters and risk factors. Brachial-ankle PWV (baPWV) and DVP signals were obtained from a Pulse Trace PWV and Pulse Trace PCA (pulse contour analysis), respectively. The DVP (obtained by photoplethysmography), allowed stiffness (SI) and reflection indexes (RI) to be derived. The first study on 47 healthy volunteers showed that both SBP and DPB correlated significantly both with baPWV and SI. Multiple regression models of the baPWV and the waist-to-hip ratio (WHR) allowed SBP and DBP to be modeled with r = 0.838 and r = 0.673, respectively. SI results also employed WHR and modeled SBP and DBP with r = 0.852 and r = 0.663, respectively. RI did not correlate either with SBP or DBP. In order to avoid the use of ultrasound techniques to measure PWV, we then developed a custom-built system to measure PWV by
J. M. Padilla E. J. Berjano J. Sa´iz R. Rodriguez Institute for Research and Innovation on Bioengineering (I3BH), Universidad Polite´cnica de Valencia, Valencia, Spain J. M. Padilla Instituto Tecnolo´gico de Morelia, Morelia, Michoaca´n, Mexico E. J. Berjano (&) Departamento de Ingenierı´a Electro´nica, Universidad Polite´cnica de Valencia, Camino de Vera, 46022 Valencia, Spain e-mail:
[email protected] L. Fa´cila Departamento de Cardiologı´a, Hospital Provincial de Castello´n, Valencia, Spain
photoplethysmography and validated it against the Pulse Trace. With the same equipment we conducted a second pilot study with ten healthy volunteers. The best SBP multiple regression model for SBP achieved r = 0.997 by considering the heart-finger PWV (hfPWV measured between R-wave and index finger), WHR and heart rate. Only WHR was significant in the DBP model. Our findings suggest that the hfPWV photoplethysmography signal could be a reliable estimator of approximate SBP and could be used, for example, to monitor cardiac patients during physical exercise sessions in cardiac rehabilitation. Keywords Blood pressure Digital volume pulse Pulse wave velocity Photoplethysmography Stiffness index Reflection index
Introduction Noninvasive monitoring of blood pressure (BP) is usually performed by traditional sphygmomanometry. However, this technique is unable to monitor short-term changes, so that a noninvasive method of measuring beat-to-beat BP would be extremely valuable. This is especially crucial in monitoring cardiac patients undergoing treadmill exercise tests during cardiac rehabilitation. It has been suggested that rate-pressure product (i.e., heart rate 9 systolic blood pressure) is the key monitoring parameter during this procedure (Pierson et al. 2004; Nieminen et al. 2008). Our objective was therefore to assess the potential use of certain cardiovascular signals (obtained non invasively) as estimators of systolic (SBP) and diastolic (DPB) blood pressure. Since a near linear correlation exists between transit time TT (measured from the R wave) and BP, this parameter could be used as a surrogate marker of
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pressure (Geddes et al. 1981; Chen et al. 2000). However, in order to obtain more general and reproducible results, we initially chose two cardiovascular signals, the pulse wave velocity (PWV) and the digital volume pulse (DVP), obtained from commercially available equipment. PWV is known to be an indicator of arterial stiffness (Lehmann 1999) and is largely determined by BP. A regional estimation of the properties of the arterial wall can be made by measuring local PWV, for instance in the aorta, where an invasive recording technique is used to record the aortic pulse wave velocity (aPWV) (Liu et al. 2004). Finally, brachial-ankle pulse wave velocity (baPWV) is a non invasive PWV measurement system which shows a good correlation with aPWV obtained by invasive recording (Yamashina et al. 2002). Digital Volume Pulse (DVP) is an alternative method of measuring arterial stiffness (Chowienczyk et al. 1999) that provides valid information on the pressure pulse waveform (Millasseau et al. 2002). DVP can be recorded in the finger by photoplethysmography (PPG) using an infrared optical transducer. It usually exhibits an early systolic peak and a later peak or point of inflection that occurs a short time (DTDVP) after the first peak in early diastole (see Fig. 1). It can provide two indexes: stiffness index (SI), which relates with large artery stiffness, and the reflection index (RI), which relates with vascular tone (Millasseau et al. 2002). The DVP waveform is determined mainly by the characteristics of systemic circulation, including pressure wave reflection and PWV of pressure in the aorta and large arteries (Chowienczyk et al. 1999). To date, there have been no previous studies employing either baPWV or DVP to estimate BP. The aim of this work was to asses the potential use of pulse wave velocity (PWV) and digital volume pulse (DVP) as estimators of systolic (SBP) and diastolic (DPB) blood pressure in healthy subjects.
Fig. 1 a Digital Volume Pulse (DVP) waveform obtained by photoplethysmography. It consists of a systolic peak (a) and diastolic peak (b) separated by a transit time delay (DTDVP). b Arterial system diagram highlighting the aortic segment: the systolic peak of DVP waveform corresponds to the blood forward wave (Fw) while the diastolic peak characterizes the blood reflected wave(s) (Rw) in the arterial system
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Materials and Methods This study was divided into two phases. In the first, the potential use of brachial-ankle PWV (baPWV) and digital volume pulse (DVP) were studied as estimators of systolic (SBP) and diastolic (DPB) blood pressure. Both signals were obtained from commercially available equipment. In the second phase, we studied the potential use of other signals related with PWV and obtained by means of custom built-equipment based on photoplethysmography. First Phase The first phase involved 47 healthy volunteers (33.78 ± 9.04 years). The subjects were examined in the supine position, with electrocardiogram electrodes placed on both wrists after a 15 min rest. Blood pressure, baPWV and DVP were measured three times for averaging. baPWV was recorded by Pulse Trace PWV (Micro Medical, Kent, UK). This equipment uses a 4 MHz Doppler probe to identify the arrival of the arterial pulse. The arterial pulse waveform was sequentially measured in two locations of the arterial tree (right brachial and right ankle) and was timed using the R wave of the ECG (Lead I) as shown in Fig. 2. The Pulse Trace PWV automatically computed the transit time (DTPWV) from the onset time difference between brachial and ankle waveforms. baPWV was then calculated by dividing the externally measured distance between brachial-ankle locations (Lba) by the DTPWV: ð1Þ baPWV ¼ Lba = DTPWV The distance Lba between the measuring sites (i.e., the brachial and posterior tibial arteries) was measured with a tape measure. To reduce the influence of body contours on the distance, the tape measure was held above the surface of the body parallel to the plane of the examination table. Five
Cardiovasc Eng
Fig. 2 Transit time (DTPWV) was computed from the onset time difference between the signals detected in two sites of measurement: the brachial and ankle arteries. The waveforms were obtained by means of an ultrasound sensor and are shown as brachial and ankle waveforms. The R wave of ECG was used by the Pulse Trace PWV (Micro Medical, Kent, UK) as timing reference to calculate DTPWV automatically, since only one ultrasound sensor was employed. TS and TD are systolic and diastolic period, respectively. The figure shows an actual register obtained in our study
partial distances were measured, as shown in Fig. 3: (1) from suprasternal notch to inferior edge of the umbilicus; (2) from the inferior edge of the umbilicus to the iliac crest; (3) from to the iliac crest to the medial malleolus in sampling site on the right leg; (4) from suprasternal notch to head of humerus; and (5) from head of humerus to the forearm. Lba was then calculated by subtracting the distance between the suprasternal notch to medial malleolus site on the posterior tibial artery from the distance between the suprasternal notch to the forearm site on the brachial artery. DVP waveform was obtained by using a Pulse Trace PCA (Micro Medical, Kent, UK) which employs a photoplethysmography (PPG) transducer. This techniques follows the changes in the reflectance of infrared light (k = 940 nm) through the finger pulp. The PPG transducer was placed on the index finger of the right hand. The Pulse Trace PCA provides two indexes from the DVP: the Stiffness Index (SI) and Reflection Index (RI). SI (measured in m/s) is defined as the ratio between subject’s height (h) and DTDVP (see Fig. 1a): SI ¼ h =DTDVP
ð2Þ
The reflection index (RI) (measured as %) is defined as the ratio between the amplitudes of the second (b) and first (a) peaks of the DVP waveform (see Fig. 1a): RI ¼ b=a
ð3Þ
Regarding the risk factors, the waist to hip ratio (WHR) was calculated as the minimal abdominal circumference
Fig. 3 Partial distances employed to calculate the right brachial– ankle distance (Lba). Lba was calculated as the sum of the partial distances 1, 2 and 3, minus the sum of the partial distances 4 and 5 (Lba = 1 ? 2 ? 3 - 4 - 5). The partial distances were (1) from suprasternal notch to inferior edge of the umbilicus; (2) from the inferior edge of the umbilicus to the iliac crest; (3) from the iliac crest to the medial malleolus in the sampling site on the right leg; (4) from suprasternal notch to head of humerus; and (5) from head of humerus to the forearm
between the xiphoid process and iliac crests (i.e., waist) divided by the circumference over the femoral heads (i.e., hips). The body mass index (BMI) was calculated as the ratio of body weight (kg) to the square of height (m). BP was measured using an automatic sphygmomanometer Omrom M6 (Omrom Healthcare, Milton Keynes, UK), following the guidelines of the European Society of Cardiology and Hypertension (2003). Age and current smoking status were determined by questionnaire. Preliminary tests (in order to design the experimental set-up) showed the marked influence of ambient temperature on the hemodynamic parameters (blood pressure, PWV and DVP). This parameter was therefore carefully controlled during the tests and kept at 21 ± 1°C. The acquisition of the DVP signal was an automatic process, i.e., the Pulse Trace PCA (Micro Medical, Kent, UK) only showed DVP values when the recording was of sufficient quality, which was achieved after pre-warming the subject’s finger.
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Second Phase In order to avoid the use of ultrasound techniques to measure PWV, in the second part of the study we designed a system to measure PWV by photoplethysmography instead of using a Doppler probe. The equipment (see Fig. 4) consisted of a signal conditioning module connected to a data acquisition card controlled by LabView software (National Instruments, Austin, TX, USA). The module processed an ECG signal (Lead I), and two photoplethysmography signals which registered the changes in the transmittance of infrared light through the index finger (index finger pulse wave) and the big toe (big toe pulse wave). With these three signals, we defined three new parameters: ftPWV (PWV measured between index finger and big toe), hfPWV (PWV measured between heart -R wave of ECG- and index finger), and htPWV (PWV measured between heart and big toe). In each case PWV was calculated by dividing the externally measured distance between each location by the recorded pulse transit time (PTT). In the case of photoplethysmography signals, PTT was measured by the onset of each pulse. The Fig. 5 shows typical waveforms obtained from the specially designed equipment, and the PTT measured for each signal. These signals provided an additional stiffness index and an additional reflection index, which were compared to those obtained from the Pulse Trace PCA (Micro Medical, Kent, UK) for validation. After validating the system, we conducted the second phase of the study on ten healthy volunteers (30.22 ± 7.06 years). The measuring protocol was similar to the first phase, but included measuring the new parameters. Statistical Analysis All statistical analyses were performed using SPSS 13 (SPSS, Chicago, IL, USA). Data were expressed as
mean ± standard deviation. Pearson0 s correlation coefficients (r) were calculated to compare the relationships between the variables under study and risk factors. Values of P \ 0.05 were considered to indicate statistical significance. Multiple regression analyses were automatically conducted by a stepwise regression method (backward elimination). Once the models had been obtained, they were tested by means of the Durbin-Watson statistic, Fisher-Snedecor statistic, variance inflation factor (VIF), and the condition index.
Results First Phase Overview The first phase of the study evaluated the relationships between cardiovascular parameters, cardiovascular measurements (baPWV, SI and RI), and risk factors. Table 1 shows the basic characteristics of the 47 healthy subjects in this group. The cardiovascular parameters included SBP, DBP, mean blood pressure (MBP), pulse pressure (PP) and heart rate (HR). The values of the variables under study obtained were: baPWV of 8.4 ± 1.4 m/s, SI of 6.28 ± 0.83 m/s, and RI of 68.53 ± 8.07%. Table 2 shows the correlation coefficients and significance between baPWV, SI, RI, cardiovascular parameters and risk factors. Assessment of baPWV baPWV was positively and significantly correlated with all the cardiovascular parameters and presented moderately high correlation coefficients with SBP (r = 0.57, P = 0.0001), DBP (r = 0.55, P = 0.0001) and MBP (r = 0.60, P = 0.0001). It was also positively and significantly correlated with some risk factors, such as BMI (r = 0.48, P = 0.001), WHR (r = 0.58, P = 0.0001) and
Fig. 4 Custom-built equipment to measure PWV by means of photoplethysmography. a The equipment consisted of a computer with a data acquisition card and a signal conditioning module for ECG and photoplethysmography signals. b Inner view of the signal conditioning module
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Cardiovasc Eng Fig. 5 Typical waveforms measured by the custom-built equipment: ECG and two photoplethysmography signals at index finger and big toe. The pulse transit time (PTT) measured between index finger and big toe was used to calculate ftPWV (a), PTT between heart (wave R of ECG) and index finger to calculate hfPWV (b), and PTT between heart and big toe to calculate htPWV (c)
Table 1 Basic characteristics of the healthy subjects who took part in the first phase of the study (n = 47)
Cardiovascular parameters
Risk factors
Characteristic
Mean
SD
Table 2 Correlation coefficients (r) and statistical significance (P) between cardiovascular parameters, risk factors, brachial-ankle pulse wave velocity (baPWV), stiffness index (SI) and reflection index (RI) in the first phase of the study (47 healthy volunteers) (significant values are highlighted in bold)
SBP (mmHg)
114.37
11.70
DBP (mmHg)
67.93
6.83
baPWV
MBP (mmHg)
82.86
9.00
r
PP (mmHg)
46.37
8.06
HR (bpm)
62.00
8.38
SBP
0.57
0.0001
0.41
0.004
0.07
[0.05
DBP
0.55
0.0001
0.40
0.005
0.16
[0.05
Age (years)
33.78
9.04
BMI (kg/m2) WHR (cm/cm)
24.14 0.86
4.12 0.10
Waist (cm)
85.38
13.36
SBP systolic blood pressure, DBP diastolic blood pressure, MBP mean blood pressure, PP pulse pressure, HR heart rate, BMI body mass index, and WHR waist to hip ratio
waist (r = 0.60, P = 0.0001). The lack of correlation between age and baPWV was unexpected, since baPWV assesses arterial stiffness and this should correlate with age. We therefore looked for confusion variables in the analyzed sample (i.e., variables which distort the relationship between baPWV and age) and we repeated the correlation analysis but excluding volunteers who smoked more than
SI P
r
RI P
r
P
MBP
0.60
0.0001
0.44
0.002
0.13
PP HR
0.35 0.36
0.018 0.014
0.24 0.29
[0.05 0.046
-0.04 -0.47
0.40
0.005
0.56
0.0001
RI
0.10
SI
0.55
Age
0.28*
[0.05
0.40
0.0001 [0.05*
[0.05
0.13
[0.05 [0.05 0.001 0.005 [0.05
BMI
0.48
0.001
0.19
0.16
[0.05
WHR
0.58
0.0001
0.33
0.026
0.14
[0.05
Waist
0.60
0.0001
0.35
0.017
0.22
[0.05
SBP systolic blood pressure, DBP diastolic blood pressure, MBP mean blood pressure, PP pulse pressure, HR heart rate, BMI body mass index, and WHR waist to hip ratio * Correlation between age and baPWV became significant (r = 0.37, P \ 0.05) when 11 smokers and BMI [ 30 kg/m2 volunteers were excluded from the analysis
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three cigarettes a day and 11 subjects with BMI [ 30 kg/ m2. In this case, we did find a significant relationship between baPWV and age, with a correlation coefficient r = 0.37 (P \ 0.05). In the multiple regression analyses for SBP and DBP, using baPWV, the best models included baPWV and the waist to hip ratio (WHR) as independent variables. The regression models were: SBP ¼ 25:43 þ 83:10 WHR þ 2:1 baPWV DBP ¼ 26:33 þ 32:70 WHR þ 1:68 baPWV with correlation coefficients of r = 0.838 and r = 0.673, respectively. Assessment of DVP (SI and RI) The stiffness index derived from DVP (SI) was positively and significantly correlated with moderately high correlation coefficients with SBP (r = 0.41, P = 0.004), DBP (r = 0.40, P = 0.005), MBP (r = 0.44, P = 0.002) and with a low correlation coefficient with HR (r = 0.29, P \ 0.05). SI was also positively and significantly correlated with the risk factors WHR (r = 0.33, P \ 0.03), waist (r = 0.35, P \ 0.02) and age (r = 0.56, P \ 0.001), but did with BMI. In contrast, RI did not correlate either with blood pressure parameters (SBP, MBP DBP) or risk factors (BMI, WHR, waist and age) (see Table 2). RI was negatively and significantly correlated only with HR (r = -0.47, P = 0.001). The results also showed that baPWV was positively and significantly correlated with SI (r = 0.55, P = 0.001), but not with RI. On the other hand, SI was positively and significantly correlated with RI (r = 0.40, P = 0.005). In the multiple regression analyses for SBP and DBP, using SI and RI, the best models included SI and the waist to hip ratio (WHR) as independent variables. The regression models were: SBP ¼ 17:84 þ 72:23 WHR þ 2:7 SI
shows the basic characteristics of the 10 healthy volunteers who took part in the second part of the study. Table 4 shows the values of the signals obtained in the study measured by Pulse Trace PCA and PWV (Micro Medical, Kent, UK) and by the custom-built equipment. In the multiple regression analyses for SBP, using the variables measured by the custom-built equipment, the best model included hfPWV, the parameter waist to hip ratio (WHR), and heart rate (HR) as independent variables. The regression model was: SBP ¼ 26:93 þ 51:49 WHR þ 15:82 hfPWV 0:375 HR with a correlation coefficient r = 0.997. In contrast, the best model for DBP only included the waist to hip ratio (WHR) as an independent variable: DBP ¼ 36:89 þ 38:90 WHR with a correlation coefficient r = 0.758.
Discussion Assessment of the Brachial-Ankle Pulse Wave Velocity (baPWV) The baPWV value obtained in our study (8.40 ± 1.42 m/s) was lower than those previously reported: 11.08 ± 1.87 m/ s (Shiotani et al. 2005), 11.15 ± 1.04 m/s (Naidu et al. 2005), 11.04 ± 1.26 m/s (Liu et al. 2005), and 16.23 ± 0.93 m/s (Nakamura et al. 2003). In these studies, baPWV was measured by techniques completely different to ours: while Shiotani et al. (2005), Liu et al. (2005), and Nakamura et al. (2003) used a volume-plethysmographic system, and Naidu et al. (2005) employed a custom-made
Table 3 Basic characteristics of the healthy volunteers considered in the second phase of the study (n = 10)
DBP ¼ 19:17 þ 42:03 WHR þ 2:1 SI with correlation coefficients of r = 0.852 and r = 0.663, respectively.
Cardiovascular parameters
Second Phase For the second phase of the study, we designed special equipment to measure PWV by photoplethysmography. We conducted a validation test against the Pulse Trace PCA and PWV by measuring the parameters SI and transit time (TT), respectively. The correlation coefficients between the measurements from both systems were 0.88 (P = 0.004) for SI, and 0.744 (P = 0.034) for TT. Table 3
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Risk factors
Characteristic
Mean
SD 12.45
SBP (mmHg)
111.25
DBP (mmHg)
69.37
6.80
MBP (mmHg)
78.87
16.59
PP (mmHg)
41.80
8.02
HR (bpm)
62.00
8.38
Age (yrs)
30.22
7.06
BMI (kg/m2)
23.05
4.08
WHR (cm/cm) Waist (cm)
0.83
0.12
81.67
16.58
SBP systolic blood pressure, DBP diastolic blood pressure, MBP, mean blood pressure, PP pulse pressure, HR heart rate, BMI body mass index, and WHR waist to hip ratio
Cardiovasc Eng Table 4 Values of the signals obtained in the second phase of the study on 10 healthy volunteers measured by both Pulse Trace PWV (Micro Medical, Kent, UK) and custom-built equipment
has previously been suggested (McVeigh et al. 1997; Yufu et al. 2007).
Measuring equipment
Parameter
baPWV as an Estimator of Blood Pressure
Pulse trace PCA and PWV (Micro Medical, Kent, UK)
baPWV (m/s)
7.91
0.89
SI (m/s)
6.13
0.76
67.73 6.94
10.52 1.20
Custom-built equipment
RI (%) ftPWV (m/s)
Mean
SD
hfPWV (m/s)
4.12
0.49
htPWV (m/s)
4.97
0.59
SI* (m/s)
6.15
1.20
RI* (%)
67.32
9.06
BaPWV brachial-ankle pulse wave velocity, SI stiffness index, RI reflection index (these two parameters measured from Pulse Trace PWV of Micro Medical, Kent, UK), ftPWV index finger-big toe pulse wave velocity, hfPWV heart-index finger pulse wave velocity, htPWV heart- big toe pulse wave velocity, SI* stiffness index; RI* reflection index (both parameters measured from the custom-built equipment)
device based on the oscillometric method. The discrepancy between our absolute baPWV value and those from the previous studies could be due to the different measuring techniques. In the relationship between baPWV and the cardiovascular parameters (see Table 2), we observed a moderately high correlation between baPWV and BP (r = 0.57 with SBP, and r = 0.55 with DBP). These values are in general inside the range of correlation coefficients previously reported for MBP by Shiotani et al. (2005) (r = 0.65, P \ 0.001), and for SBP by Nakamura et al. (2003) (r = 0.643, P \ 0.0001) and Liu et al. (2005) (r = 0.32, P \ 0.05). The correlations found between baPWV and risk factors in the healthy volunteers in our study were partially in disagreement with previous studies. We observed a moderate correlation between baPWV and BMI (r = 0.48), which is in agreement with Liu et al. (2005) (r = 0.46, P \ 0.05), but not with Shiotani et al. (2005), who did not find any correlation. We also found a significant correlation with WHR (r = 0.58), which had not been reported previously. Initially, we did not find a significant correlation between baPWV and age. However, since previous studies on healthy volunteers had found high correlations between baPWV and age (Nakamura et al. 2003; Liu et al. 2005), we repeated the correlation analysis but excluding 11 smokers and BMI [ 30 kg/m2 volunteers. In this case, we did find a significant relationship between baPWV and age, with a correlation coefficient r = 0.34. Since our population was composed of 70% males, our results are in good agreement with those published by Im et al. (2007), who found r = 0.30 in men and r = 0.11 in women. Our results also confirm that smoker and BMI [ 30 kg/m2 status could possibly be confusion variables in this type of analysis, as
To our knowledge, this is the first study where the baPWV signal was employed to estimate SBP and DBP. However, our findings are comparable to those obtained in similar studies. For instance, Millasseau et al. (2002) reported a regression model to infer the carotid-femoral PWV (cfPWV) by using age and mean blood pressure (MBP), and found a correlation coefficient of r = 0.71 (P = 0.0001). Asmar et al. (1995) also reported a regression model with healthy volunteers in which cfPWV was explained by age and SBP (r = 0.685, P = 0.001). On the other hand, Kubo et al. (2002) reported different models to explain baPWV by using age and MBP in cardiac patients. Our results are partially in agreement with those obtained by Millasseau et al. (2002) and Kubo et al. (2002) since the highest correlation coefficient found in our study was also for MBP (r = 0.60) (see Table 2). However, our interest is focused on models that explain SBP and DBP by using other cardiovascular parameters, and in this respect, our findings are hence new. Assessment of the Digital Volume Pulse (DVP) The SI value found in our study (6.28 ± 0.83 m/s) was lower than those previously reported by Millasseau et al. (2002) (8.4 m/s) and by Alty et al. (2007) (9.4 m/s), both on healthy subjects. Since age is known to affect SI, this disagreement could be due to age differences between our subjects (33.78 ± 9.04 years) and those studied by Millasseau et al. (2002) (47 ± 13.8 years) and Alty et al. (2007) (50 ± 13.6 years). We observed a moderate correlation between SI and blood pressure (r = 0.41 for SBP, r = 0.40 for DBP and r = 0.44 for MBP), which is in agreement with that found by Millasseau et al. (2003) (r = 0.32 for SBP, r = 0.48 for DBP and r = 0.45 for MBP) in a group of healthy subjects. Concerning the relationship between SI and risk factors in healthy volunteers (see Table 2), our results showed a moderately high correlation between SI and age (r = 0.56), which was similar to that previously reported by Millasseau et al. (2003) (r = 0.63). We also observed that SI presented a low correlation with WHR (r = 0.33), and was slightly higher with waist measurement (r = 0.35). Finally, we did not find any correlation between SI and BMI. These relationships had not been studied previously. Mean RI value found by us in healthy volunteers was 68.53 ± 8.07%, which is in agreement with that reported by Chowienczyk et al. (1999), also in healthy volunteers (60.00 ± 5.50%). In our study RI did not correlate either
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with SBP, DBP, MBP or with PP (see Table 2), which is also in agreement with Millasseau et al. (2003). We also found a significant negative correlation between RI and HR (r = -0.473, P = 0.001), which has also been previously reported by Chowienczyk et al. (1999) and Millasseau et al. (2003). Finally, even though the relationship between baPWV and SI had not been studied previously, the correlation we found (r = 0.55, P = 0.0001) was similar to that found by Millasseau et al. (2002) between carotid-femoral PWV (cfPWV) and SI (r [ 0.65, P \ 0.01). The relationship between PWV and arterial stiffness has been previously discussed (Hamilton et al. 2007). In addition, by using the Moens–Kortweg formula, it is know that PWV is proportional to the square root of the elastic modulus of the arterial wall. However, it has been also pointed out that the square root relationship also means that a change in PWV is not a particularly sensitive measure of change in physical arterial properties (Hughes et al. 2004). DVP as an Estimator of Blood Pressure Only the SI parameter derived from DVP was found to be useful in the multiple regression models. Once more, and although there are no previous studies with which to compare our SBP and DBP models, we should point out that Millasseau et al. (2002) reported a model with a high correlation coefficient (r = 0.69, P = 0.0001) in which SI was explained both by age and by MBP. This is in close agreement with our experimental results, in which the highest correlation coefficients were between SI and age, and between SI and MBP.
exercise in cardiac rehabilitation. Unfortunately, since DBP was only explained by the WHR, this means that we did not find any signal that could potentially be used to estimate DBP. Finally, future work should be conducted with subjects undergoing physical exercise to assess the reliability of our current results.
Conclusions The findings of this study suggest that: (1)
(2)
(3)
(4)
Both the baPWV and SI signals (measured from DVP) are good estimators of SBP and DBP. In contrast, RI (measured from DVP) does not significantly correlate with any blood pressure parameter. Estimation of SBP and DBP in multiple regression analysis is improved by including the waist to hip ratio (WHR). In this case, similar correlation coefficients are obtained from baPWV and SI. Results are improved when new variables measured by the custom-built equipment are considered in the multiple regression analyses. The best model to explain the SBP (r = 0.997) includes the hfPWV (PWV measured by photoplethysmography between R-wave of ECG and index finger), along with the waist to hip ratio (WHR) and heart rate (HR) as independent variables. DBP could not be explained by any signal from the custom-built equipment. These results suggest that hfPWV could be a good estimator of the beat-to-beat SBP, which could be very useful for monitoring cardiac patients during physical exercise in cardiac rehabilitation.
hfPWV as an Estimator of Blood Pressure First of all, we found a high correlation coefficient (r = 0.88) between the SI measured by the custom-built equipment and that measured by the Pulse Trace PCA (Micro Medical, Kent, UK), which suggests that both the transmittance photoplethysmography technique (in the custom-built equipment) and the reflectance technique (Pulse Trace PCA) offer similar results. Moreover, the mean ftPWV value measured in our study (6.94 ± 1.20 m/ s) was similar to those reported in healthy volunteers by Tsai et al. (2005) (6.39 ± 0.93 m/s) and Chen et al. (2004) (6.49 ± 0.92 m/s). Secondly, in the multiple regression analyses for SBP the best model included hfPWV, waist to hip ratio (WHR), and heart rate (HR) as independent variables. The high correlation coefficient (r = 0.997) given by this model suggests that hfPWV could be a good estimator of the beatto-beat SBP. hfPWV could therefore be useful, for instance, for monitoring cardiac patients during physical
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Acknowledgments The translation of this paper was funded by the Universidad Polite´cnica de Valencia, Spain. This study was partially supported by Merce´ V. Electromedicina (Valencia, Spain) and by ‘‘IMIDT-Programa de Investigacio´n y Desarrollo Tecnolo´gico’’ of the ‘‘Instituto de la Mediana y Pequen˜a Industria de la Comunidad Valenciana (IMPIVA)’’.
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