Continuous Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio

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1 Continuous Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio Xiao-Rong Ding, Student Member, IEEE, Yuan-Ting Zhang, Fellow, IEEE, Jing Liu, Wen-Xuan Dai, Hon Ki Tsang*, Senior Member, IEEE Abstract Pulse transit time (PTT) has attracted much interest for cuffless blood pressure (BP) measurement. However, its limited accuracy is one of the main problems preventing its widespread acceptance. Arterial BP oscillates mainly at high frequency (HF) because of respiratory activity, and at low frequency (LF) because of vasomotor tone. Prior studies suggested that PTT can track BP variation in HF range, but was inadequate to follow the LF variation, which is probably the main reason for its unsatisfactory accuracy. This paper presents a new indicator, the photoplethysmogram intensity ratio () which can be affected by changes in the arterial diameter and thus trace the LF variation of BP. Spectral analysis of BP, PTT, and respiratory signal confirmed that PTT was related to BP in HF at the respiratory frequency, while was associated with BP in LF range. We therefore develop a novel BP estimation algorithm by using both PTT and. The proposed algorithm was validated on 7 healthy subjects with continuous Finapres BP as reference. The results showed that the mean ± standard deviation (SD) for the estimated systolic, diastolic and mean BP with the proposed method against reference were -.37±5.1 mmhg, -.8±4.6 mmhg, -.18±4.13 mmhg, and mean absolute difference (MAD) were 4.9 mmhg, 3.18 mmhg, 3.18 mmhg, respectively. Furthermore, the proposed method outperformed the two most cited PTT algorithms for about mmhg in SD and MAD. These results demonstrated that the proposed BP model using and PTT can estimate continuous BP with improved accuracy. Index Terms Arterial diameter change, cuffless blood pressure, photoplethysmogram intensity ratio, pulse transit time, respiration, vasomotion This work was supported in part by the Guangdong Innovation Research Team Fund for Low-cost Healthcare Technologies in China, the External Cooperation Program of the Chinese Academy of Sciences (Grant GJHZ11). Asterisk indicates corresponding author. Xiao-Rong Ding, Yuan-Ting Zhang, Jing Liu, and Wen-Xuan Dai is with the Joint Research Centre for Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China ( xrding@ee.cuhk.edu.hk; ytzhangapple@icloud.com; jingliu@ee.cuhk.edu.hk; wxdai@ee.cuhk.edu.hk) *Hon Ki Tsang is with the Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China ( hktsang@ee.cuhk.edu.hk). Copyright (c) 15 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an to pubs-permissions@ieee.org. B I. INTRODUCTION LOOD pressure (BP) is an important hemodynamic parameter varying between systolic BP (SBP) to diastolic BP (DBP) in each heartbeat. High BP, also known as hypertension, is one of the major modifiable risk factors leading to the development of cardiovascular diseases (CVDs) the number one killer in the world. Hypertension is highly prevalent but poorly controlled because of the low awareness and treatment rate [1], which enhances development of CVDs and results in significant burdens on individuals and society. BP variability (BPV) has been reported to have prognostic value for hypertension [], and thus continuous BP measurement is crucial for early prevention, detection, evaluation and treatment of hypertension and related CVDs. Conventional 4-hour ambulatory BP monitoring can facilitate monitoring of BPV through measuring BP at regular intervals with auscultatory or oscillometric approaches. However, it has limitations including the discontinuous nature and the discomfort caused by the repeated cuff inflations. Compared with cuff-based BP techniques, pulse transit time (PTT) method has received much attention over the recent decades because of its capability to track BP change, as well as its advantages as a noninvasive, continuous and most importantly cuffless tool for BP measurement [3-5]. PTT is the time taken by the arterial pulse propagating from the heart to a peripheral site, and can be calculated as the time interval between the R wave peak of electrocardiogram (ECG) and a characteristic point of photoplethysmogram (PPG). The fundamental principle of PTT-based method is based upon the pulse wave velocity (PWV) recording through the Moens-Korteweg (M-K) equation: Eh PWV (1) d which relates PWV with the elastic modulus of vessel wall E, blood density ρ and arterial dimension properties such as vessel thickness h and arterial diameter d. PWV is inversely related with PTT, i.e., PWV=K/PTT, where K is the distance between heart and certain peripheral site; and E can be exponentially correlated BP through the following equation [6]:

2 E E e () P where E is the elastic modulus at zero pressure; γ is a coefficient depending on particular vessel, and is BP. Therefore, PTT can be translated into BP with an initial calibration under the assumption that h/d keeps constant. PTT-based BP estimation has been extensively studied ever since [7-], when Chen et al [7] estimated SBP using pulse arrival time with intermittent calibration, and showed that the estimated SBP was highly correlated with reference SBP (r=.97±.). In 5, Poon et al [9] established a model with PTT to estimate BP with initial calibration, and achieved an accuracy of.6±9.8 mmhg and.9±5.6 mmhg for SBP and DBP, respectively. Recently, Wibmer et al [19] investigated the relationship of PTT and SBP through regression analysis and found a nonlinear approach was better than linear one. Although PTT has been considered a promising surrogate of BP and could become the most widely used technique for noninvasive continuous BP monitoring in the future [4, 5], there are still several problems to be solved before its widespread application. Frist, some PTT-BP models could only provide one BP parameter, e.g., exclusively SBP [7, 8, 1, ], DBP [11], or mean BP (MBP) [18], but SBP, DBP, and MBP all have clinical significance. Second, a calibration procedure is required to map PTT to BP. However, re-calibration at intermittent intervals is often necessary for accurate estimation, potentially owing to the inadequacy of PTT to track BP variation over a long period. Last and most importantly, the accuracy of PTT-based BP estimation is unsatisfactory. The possible reasons are the influences of the vascular or vasomotor tone and the pre-ejection period (PEP) [3]. Regarding PEP issue, impedance cardiogram (ICG), phonocardiogram (PCG) [], ballistocardiogram (BCG) or two peripheral PPG have been adopted to eliminate the effect of PEP. Nonetheless, several studies indicated that PTT with PEP included actually performs better for BP estimation than that with PEP removed, which demonstrates the positive effect of PEP on BP estimation [8,, 3]. For the vasomotor tone, previous research has examined its influence on BP-PTT relationship [4], and central PPG instead of peripheral PPG was suggested to alleviate such effect [18]. However, few studies have attempted to take this factor into account in the PTT-BP estimation model to improve the accuracy. BP is dynamic and its rhythmic oscillations can be identified with the appearance in its spectrum as individual peaks, which reflect: (1) the oscillations with a frequency typically between.-.35 Hz, a frequency similar to that of normal respiratory activity, defined as high frequency (HF); () oscillations with a frequency of approximately Hz, suggesting the sympathetic modulation of vasomotor tone, defined as low frequency (LF) [5-7]. According to our prior research work [8, 9], PTT could track BP in HF range, but was inadequate to follow LF variations in BP. This is probably the most important reason for the inaccuracy of estimated BP with only the PTT and the requirement of intermittent calibration to maintain accuracy. However, to date, few study has addressed the LF component in cuffless BP estimation. Here we propose a new indicator, the PPG intensity ratio () [3], that can reflect changes in the arterial diameter and thereby the arterial vasomotion, and thus allow tracking of BP in the LF range. Furthermore, we develop a novel BP estimation algorithm which employs both the and the PTT to improve the estimation accuracy. II. METHODOLOGY A. PTT and Fig. 1 illustrates the diagram of PTT and calculation, where PTT is determined as the time interval between the R wave peak of ECG and the peak of first derivative of PPG in the same cardiac cycle, and is the ratio of PPG peak intensity I H to PPG valley intensity I L of one cardiac cycle. Our earlier study shows that can theoretically reflect the arterial diameter change d during one cardiac cycle from systole to diastole, and is exponentially linked with d through the following expression [3]: d e (3) where α is considered to be a constant related to the optical absorption coefficients in the light path. Fig. 1. Diagram of pulse transit time (PTT) and photoplethysmogram (PPG) intensity ratio () calculation, where I H indicates PPG peak intensity, I L the valley intensity, and 1st dppg is the first derivative of PPG. From a physiological perspective, BP is mainly affected by four factors: arterial compliance, cardiac output, peripheral resistance, and blood volume [31]. Arterial compliance can be evaluated by PTT, since PTT is an index of arterial stiffness [3]. Also, cardiac output can be related to PTT through heart rate. With regard to peripheral resistance and blood volume, one of the primary sources is the arterial diameter change which can be assessed by as described above. Accordingly, PTT and can capture BP variations indirectly, and be used for BP estimation.

3 B. Model-based BP Estimation with PTT and Arterial BP is a hemodynamic parameter that fluctuates on a beat-to-beat basis as a result of the dynamic interplay involving vasomotion, arterial mechanisms and neural regulation [33]. Beat-to-beat BP fluctuations are usually attributed to two rhythmic events: respiration and vasomotion [34-36]. The respiratory rhythm is a HF spectral component that occurs in BP variability, and also considered to be a marker of vagal modulation, whereas the slow oscillation, corresponding to the vasomotor waves, is a LF component that is present in BPV, and is also a marker of sympathetic modulation. Physiological study of BPV showed that both the slow variability and fast variability can be observed in SBP, while the DBP exhibits only on the slow variability [37]. Since SBP is the summation of pulse pressure (PP) and DBP, it is hypothesized that the HF component is mainly dominant in PP. Furthermore, PTT and have been investigated to reflect HF and LF component in BPV [3], respectively. It is therefore speculated that PP and DBP can be derived from PTT and, respectively, and consequently the SBP can be estimated accurately. 1) PP Estimation with PTT Based on M-K Equation In the M-K equation (1), the elastic modulus E is given by [38]: P E R O 1 RO Ri (4) RO Ri where R O is the external radius, R O is the external radius change in response of the pressure change P, and P is the PP in the artery; Ri is the internal radius, and σ is the Poisson s ratio. PWV is reversely proportional to PTT: 1 PWV (5) PTT Since the artery radius change is quite small compared with elastic modulus change, it is assumed to be constant. According to (1) and (4)-(5), P has a relationship with PTT as follows: Fig.. The two-element Windkessel [39]. In a pure Windkessel, the DBP can be theoretically expressed in terms of RC using the following equation: DBP t / RC P e (8) where P is the end-systolic aortic pressure. Since C is constant in a relatively short period, DBP mainly varies with R. Noting that the major regulator of peripheral vascular resistance is the vessel diameter, R will mainly rely on the arterial diameter change d. As mentioned above in (3), d is related with as follows: 1 d (9) e And d is inversely related to R. Thus DBP depends on the reciprocal of : 1 DBP (1) Therefore, DBP can be derived with calibrated DBP and. 3) SBP Estimation with PP and DBP DBP DBP (11) SBP is the sum of PP and DBP. Thereupon, beat-to-beat SBP can be estimated with the addition of (7) and (11): PTT PP SBP DBP (1) PTT 1 PP (6) PTT With initial calibrated PP and PTT, PP can be derived in terms of measured PTT: PTT PP PP (7) PTT ) DBP Estimation with based on Windkessel Model Two-element Windkessel model, originally proposed by Frank, consists of peripheral resistance R and arterial compliance C [39], as shown in Fig.. C. Experiment To validate the proposed BP estimation using both PTT and as given by (11) and (1), an experiment was conducted on 7 healthy adults (14 males) with mean age of 5.6±.1 years (range 1-9 years), who were nonsmokers with no history of cardiovascular disease. Reference BP was measured by Finapres (Finapres Medical System), a noninvasive continuous BP measurement system, with the finger cuff on the left thumb, and brachial cuff on the left upper arm. ECG and PPG were acquired with one-lead ECG electrodes placed on left and right arms, and PPG sensor on left index finger, respectively. Synchronous respiratory activity continuously monitored by recording the chest movement with respiratory monitoring belt (Vernier Software & Technology). All tests were performed with subjects in the seated position, and the signals were

4 recorded at the sampling rate of 1 Hz for five minutes. All the subjects gave their informed consent prior to the experiments, in accordance with the guidelines of the Institutional Research Ethics Board. D. Signal Processing and Data Analysis In order to verify the capability of PTT to track the HF component of SBP as well as respiratory activity, and to reflect LF fluctuations of BP, the power spectrum analysis of SBP, DBP, PP, respiratory signal, PTT and were conducted in -.5 Hz frequency range based on Lomb-Scargle periodogram method [4]. Difference mean and standard deviation (SD), as well as mean absolute difference (MAD) between estimated BP with the proposed method and reference BP were used as the evaluation metrics. The agreement between reference BP and estimated BP with the proposed method were analyzed according to the Bland-Altman approach [41], with the agreement limits defined by mean ±1.96 SD. In addition, the proposed method was compared with two most cited PTT-based algorithms [7, 9] for cuffless BP estimation. Statistical significance was estimated using Student s t-test. P<.5 is regarded as statistically significant. Furthermore, time series of PP, PTT and respiratory signal with their corresponding spectrums are presented in Fig. 4. As can be observed from Fig. 4 (a)-(c), they have a similar variation pattern with almost the same frequency components at.-.3 Hz. Moreover, from the PSD curves, the respiration-synchronous variation is seen as peaks at the respiratory frequency of PP spectrum, as well as in PTT. This indicates that the HF component of PP can be reflected by PTT, which might be caused by the respiratory activity, as discussed in the next section. Example recordings of DBP and and their power spectra are shown in Fig. 5. The variation of DBP is slower compared with PP, with its frequency range concentrated at approximately.1 Hz. It can be seen from Fig. 5 (a) and (c) that the amplitude of DBP is inversely related with that of on the whole, and they have the similar spectral components. This is in line with preliminary study about which demonstrated could potentially evaluate the LF modulation of BP. But it is worth noting that there is also minor HF component appeared in spectrum. III. RESULTS A. Spectral Analysis of BP, Respiratory Signal, PTT and As can be seen in Fig. 3 (a), slow variability and fast variability can be observed in a typical continuous BP signal. Correspondingly, the variations of SBP, DBP and PP are shown in Fig. 3 (b). Obviously, SBP contained both slow variation and fast variation, whereas the DBP only showed slow variability, with PP presented the fast variability. Power spectral density (PSD) of SBP, DBP, and PP as illustrated in Fig. (c)-(e) can further describe this, where SBP showed LF variation centered at around.1 Hz, with HF variation dominated between.-.3 Hz; and the LF spectrum components were more pronounced in DBP, while PP mainly contained the HF variation. This is consistent with previous study about BP variation. Fig. 4. Time series and corresponding PSD of PP (a-b), PTT (c-d) and respiratory signal (e-f) of a representative subject. Fig. 3. Continuous BP signal (a); beat-to-beat SBP, DBP and PP (b) of a representative subject, with corresponding PSD of SBP (c), DBP (d) and PP (e). Fig. 5. Typical time series and corresponding PSD of DBP (a-b) and (c-d) of a representative subject.

5 B. Performance of Proposed BP Model with PTT and The estimation results of proposed BP model were compared with the continuous Finapres BP as the reference with regard to correlation and agreement. The correlation and the Bland-Altman plot of the SBP, DBP and MBP estimation for our proposed method versus Finapres BP are given in Fig. 6. The Person s correlation coefficient between overall estimated SBP, DBP, and MBP and that of Finapres is.91,.88, and.89, respectively. For the Bland-Altman plot, the x-axis of the plots presents the average of the estimation with the proposed algorithm and the Finapres, while the y-axis shows the difference between the two methods. The bias (mean) and the limits of agreement (bias±1.96 SD) are illustrated in red solid line and black dash dot lines, respectively. It is observed that the majority of the points lie within the limit of agreement, indicating that the estimated BP with the proposed method are in close agreement with those made by Finapres. The bias for SBP, DBP, and MBP estimates are -.37, -.8 and -.18 mmhg, respectively. Fig. 6. Correlation and Bland-Altman plots of SBP (a-b), DBP (c-d), and MBP (e-f) with the reference of Finapres BP. C. Comparison of Proposed Model against Current PTT Algorithms To further evaluate the efficiency of the proposed method, we compared the proposed method with two of the most cited PTT algorithms in the cuffless BP estimation area [7, 9]. The first PTT algorithm [7] estimates SBP in terms of relative PTT change through the following equation: SBP SBP PTT PTT PTT (13) And the second PTT algorithm [9] can obtain SBP and DBP based on the following equation set: PTT DBP MBP ln PTT 1 SBP DBP 3 PTT PTT (14a) PTT SBP DBP SBP DBP (14b) PTT Fig. 7 shows a representative example from the subjects of the beat-to-beat Finapres SBP, estimated SBP by proposed

6 algorithm with PTT and, compared to those by PTT algorithms, where SBP_PTT (1) represents the first PTT algorithm, and SBP_PTT () the second PTT algorithm. We can see that estimated SBP with proposed method tracked better with Finapres SBP compared with those of PTT methods. For this dataset with 66 beat-to-beat SBP, the beat by beat variation can be observed, and the average value measured by Finapres was ±4.87 mmhg, and the estimated values by proposed method, first and second PTT methods were ±4.8 mmhg, ±3.65 mmhg, and 1.76±7.73 mmhg, respectively. The corresponding errors were.59±3.1 mmhg, -1.83±4.6 mmhg and 5.56±5.9 mmhg, respectively, which further reveals that the SBP estimated with our method shows better correlation with Finapres SBP than the control PTT algorithms. Since the first PTT method only contains the algorithm for SBP estimation, DBP and MBP estimation were only compared with those of the second PTT method. It can be seen from Fig. 8 that DBP varied slower than SBP, where the average Finapres value was 73.5±.98 mmhg. The estimated DBP with proposed method and PTT method were 71.98±.3 mmhg and 79.38±4. mmhg, respectively, with the corresponding errors -1.7±.11 mmhg and 6.13±5.3 mmhg. Obviously, the estimation with the PTT method overestimated DBP not only in terms of the average level but the variability. Fig. 8. Estimated beat-to-beat DBP with proposed method (green) and PTT algorithm (dash blue) with the reference of Finapres DBP (red) (a) and the estimated errors (b) of one representative subject. Fig. 7. Estimated beat-to-beat SBP with proposed method (green) and PTT algorithms (solid blue and dash blue) with the reference of Finapres SBP (red) (a) and the estimated errors (b) of one representative subject. The variation of MBP is also slow which is similar to that of DBP, with average value of 87.3±3. mmhg, as depicted in Fig. 9. The estimated MBP with proposed method and the PTT method are 85.78±.3 mmhg and 93.18±5.6 mmhg, with corresponding estimation error of -1.45±.7 mmhg and 5.94±5.37 mmhg, respectively, suggesting that the estimation with the proposed method correlates better with Finapres MBP than that of the PTT method. Fig. 9. Estimated beat-to-beat MBP with proposed method (green) and PTT algorithm (dash blue) with the reference of Finapres MBP (red) (a) and the estimated errors (b) of one representative subject. Furthermore, the overall performance was analyzed in terms of difference mean, SD and MAD. Difference mean is a measure of the bias of BP estimates, while difference SD is a measure of error variability. MAD is a measure of overall accuracy in estimating BP. The smaller MAD, the better overall performance. Table I summarizes the values of mean, SD and MAD for our proposed BP estimation method and the PTT methods tested on 7 subjects, including 1713 heart beats. It is observed that the difference mean of our proposed method in estimating SBP, DBP, and MBP is within -.37, -.8, and -.18 mmhg of the Finapres, respectively; the difference SD of the proposed method in estimating SBP, DBP and MBP is

7 within 5.1, 4.6, and 4.13 mmhg of the Finapres, respectively; and the MAD of the proposed method in estimating SBP, DBP and MBP is within 4.9, 3.18, and 3.18 mmhg of the Finapres, respectively. Comparing with the PTT methods, it is evident that our proposed method in estimating SBP, DBP and MAP achieves smaller difference mean, SD, as well as MAD, and the difference is significant, indicating a better accuracy. TABLE I ACCURACY, PRECISION, AND AGREEMENT BETWEEN FINAPRES BP AND ESTIMATED BP WITH PTT, AND THAT WITH PTT AND PTT PTT Proposed Method (1) Method () Method [7] [9] SBP Mean DBP N/A (mmhg) MBP N/A SD (mmhg) MAD (mmhg) SBP * DBP N/A MBP N/A SBP * DBP N/A MBP N/A *Statistically significant at the level.5 compared with PTT method (1); statistically significant at the level.5 compared with PTT method (). IV. DISCUSSION In the present study, we proposed a new indicator, the that can reflect the arterial diameter change, to trace the LF variation of BP, and established a novel BP model with the combination of and PTT. We found that the beat-to-beat BP contained both the HF and LF variation components, where the HF component was dominant in PP and similar to that of PTT as well as respiratory signal, while the LF range was primarily in DBP and was also coupled with the. The PTT and were therefore adopted to estimated PP and DBP, respectively, and SBP can be obtained accordingly. The preliminary results on healthy subjects demonstrated the feasibility of using both PTT and to enhance the accuracy of PTT-based BP estimation. A. Effect of Respiration and Vasomotor Tone on BP It has been long recognized that arterial BP fluctuates on a beat-to-beat basis, and the application of spectral techniques to continuous BP has revealed the presence of spontaneous fluctuations including the oscillations at the HF range similar to respiratory frequency, and vasomotion waves in the LF range slower than the respiratory frequency [35, 36]. Power spectral analysis of SBP in this study was consistent with this, i.e., SBP spectrum contained both the HF peak at around.5 Hz, which was coupled with the respiratory frequency, and the LF peaks focused at approximately.1 Hz. Previous investigations demonstrated that the HF component oscillation in BP is related to the respiratory activity [4]. The underlying mechanisms of the fluctuation of BP with respiration is probably due to the intrathoracic pressure change with breathing which has a mechanical effect on venous return, pulmonary vascular and aortic pressure, thereby leading to the cyclic variation in BP. Several researchers have found that the modulation of this HF component for SBP spectrum was linearly related to the respiratory sinus arrhythmia (RSA), indicating that respiratory fluctuations in BP was attributable to RSA [43]. Moreover, Drinnan et al [44] quantified the relation between heart rate and PTT through paced respiration, and found that there was a strong relationship between PTT and heart rate interval. Johansson et al [45] confirmed that PTT varied in pace with respiration and detected the respiration rate reliably from PTT. The result of present study about the coupled frequency between PTT and respiratory signal further verified this. Besides, PTT has been used as a measure of respiratory effort [46], because PTT is inversely proportional to BP, and BP falls with inspiration with the rise in PTT. This can also be observed from time series of PP, PTT and respiratory signal as depicted in Fig. 4. With these considerations, we thus reasoned that PTT could reflect the effect of respiration activity on BP, and can be used to estimate the HF component of BP variation, particularly PP. On the other hand, the LF component of BP is associated with vasomotion waves that result from an oscillation of the sympathetic vasomotor tone. Except for the respiratory synchronous oscillations in BP, the vasomotor tone is also an essential determinant of BP. A number of investigators evidenced that the oscillations of vasomotor tone is caused by local changes in smooth muscle constriction and dilation through the modulation of the sympathetic nervous activity [35, 36]. Though the underlying mechanism has remained elusive, the fluctuations in vasomotor tone are considered to relate to the local adjustment of peripheral resistance to regulate the blood flow thus to meet the local metabolic demand. The adjustment of the peripheral resistance is mainly determined by the arterial diameter change, which is the result of the variations in the tension exerted by the smooth muscle in the vessel walls [47]. In other words, the arterial diameter change might be the primary factor that affects the vasomotor tone. is related to the arterial diameter change, and thus is hypothesized to reflect the vasomotor tone and further the LF oscillations in BP. The spectrum of and BP in this study could explain the hypothesis, which is also consistent with earlier reported results. Our previous study analyzed the relationship between and BP under the influence of autonomic nervous activities, such as deep breathing, Valsalva maneuver, and sustained handgrip, and showed that increase level of BP was associated with a shift of spectral power toward the LF component, suggesting the capability of to evaluate the modulation of sympathetic nervous activity on BP [3]. In addition, Nitzan et al [48] used a similar indicator, the relative amplitude variability of PPG signal, to evaluate autonomic nervous system, and claimed that the variation of PPG baseline and amplitude were mainly concentrated in the LF range, implying the influence of the sympathetic nervous activity. B. PTT Methods for BP Estimation PTT has been extensively studied for continuous BP

8 estimation without a cuff. Despite of its theoretical feasibility, the PTT-based BP measurement method hasn t been widely applied clinically because of several existed problems; particularly the accuracy issue remains the overarching challenge facing researchers. Prior studies have reported that PTT mainly presents the HF component variation of BP, but is inadequate to follow the LF variation [8, 9], which we hypothesize to be the utmost reason leading to the unsatisfactory accuracy of most current PTT-based BP measurement approaches. Continuous BP could be accurately tracked if the noninvasive physiological parameters used for cuffless BP estimation can not only follow the HF respiratory influence on BP, but also its LF oscillation due to the sympathetic modulation of vasomotor tone. McCarthy et al [17] examined two popular PTT-BP algorithms, which are also used in this study as contrast methods, and found that neither these two algorithms could provide reliable BP estimations over a long period. The reason for Chen s method with acceptable accuracy with intermittent calibration is probably due to the combination of LF variation of cuff-based BP with HF variation of PTT. Without the intermittent calibration, the estimation accuracy of SBP was reported to deteriorate from.64±1.55 mmhg to -3.4±9. mmhg within 1 min. As for Poon s method, the accuracy remained relatively stable, from 1.79±1.5 mmhg to 1.4±9.74 mmhg within 1 min. The larger SD after the initial calibration was potentially attributed to the HF variation of PTT for DBP estimation, as can be seen in Fig. 9 (a). However, DBP varies mainly in LF range, as shown in Fig. 5 (b). As a result, the accuracy of SBP estimation was decreased, because SBP was derived from DBP and PP. Besides, most of PTT-based approaches estimated BP with PTT through linear or nonlinear regression method, mostly for SBP estimation, rather than theoretical model based on physiological significance. And DBP has been reported to correlate less with PTT than SBP, which is probably a consequence of the smaller range of variations in DBP as found in those studies [1, 1, 16, 19]. Investigation by Liu et al [4] about the relationship between PTT and BP during exercise and recovery revealed the influence of the vascular smooth muscle tone on BP-PTT relationship, suggesting that the vascular tone should be considered into PTT-based BP estimation. Nevertheless, few studies have attempted to take account this factor into the PTT-based BP model to enhance the accuracy. The strengths of this study, compared with previous studies of PTT-based cuffless BP methods, are the introduction of a novel indicator to evaluate the LF variation of BP, and its combination with PTT to estimate BP to achieve more accurate estimations. With the LF and HF of BP evaluated by and PTT, continuous BP is supposed to be predicted accurately without frequent calibration. Moreover, despite of SBP, estimations of DBP and MBP have also been achieved with acceptable accuracy. C. Limitations There are some limitations in this study. One concern is that we validated our proposed method with Finapres monitor as the reference, while the gold standard for continuous BP measurement should be the invasive intra-arterial method. Second, the beat-to-beat estimation with the proposed method and PTT methods were only within a short period of time. The estimation with long-term period should be further validated. Third, though the effects of breathing pattern on the respiratory component of SBP spectrum have already been analyzed in previous study [49], the pattern on PTT should be further investigated. Furthermore, the utilization of to assess the vasomotor tone on BP should be further validated with more experiments. Finally, the proposed method was tested on healthy subjects, where the sample size is not large enough and subjects with CVDs were not included. V. CONCLUSION AND FUTURE WORK In summary, we have presented a new model for BP estimation using PTT and, and validated the model experimentally. We obtained more accurate results for the cuffless BP estimation by using both PTT and the new indicator, the. By means of one-point calibration, the beat-to-beat SBP, DBP and MBP can be calculated. We found that can indicate the LF variation of BP, whereas PTT reflects the HF fluctuations of BP. In addition, a novel BP model was established with the combination of and PTT, which outperformed the compared PTT-based methods in terms of accuracy on 7 subjects. Most notably, this is the first study to our knowledge to consider the use of to estimate the influence of vasomotor tone on BP. Our results provide evidence for BP variations in both the LF and the HF range where PTT can mainly reflect HF component, and indicates that LF component of BP should be considered to improve the estimation accuracy. Although the pilot study offers a potential method for estimating cuffless BP with better accuracy, it should be further validated with larger sample following with corresponding standard requirement, for example, the IEEE 178 standard for wearable cuffless BP measuring devices. With better accuracy, we expect this method to provide insight for cuffless BP estimation technique. Moreover, through continuous monitoring BP in an unobtrusive way with an acceptable accuracy permits better prevention and management of hypertension, thus reducing the global burden generated by CVDs. ACKNOWLEDGMENT The authors would like to thank the student volunteers from Dept. of Electronic Engineering, The Chinese University of Hong Kong for their participation in the experiment, and Dr. W. Karlen from Mobile Health Systems Laboratory, ETH Zurich for kindly helping to revise this manuscript. REFERENCES [1] C. K. Chow, et al., "Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries," JAMA, vol. 31, pp , 13. [] G. Parati, et al., "Assessment and management of blood-pressure variability," Nat. Rev. Cardiol., vol. 1, pp , 13. [3] D. Buxi, et al., "A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time," Physiol. Meas., vol. 36, pp. R1-R6, 15.

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