Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transform

Computers and Electrical Engineering 39 (2013) 103–111

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Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transform q Younhee Choi, Qiao Zhang, Seokbum Ko ⇑ Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada

a r t i c l e

i n f o

Article history: Available online 8 November 2012

a b s t r a c t It is widely accepted that pulse transit time (PTT), from the R wave peak of electrocardiogram (ECG) to a characteristic point of photoplethysmogram (PPG), is related to arterial stiffness, and can be used to estimate blood pressure. A promising signal processing technology, Hilbert–Huang transform (HHT), is introduced to analyze both ECG and PPG data, which are inherently nonlinear and non-stationary. The relationship between blood pressure and PTT is illustrated, and the problems of calibration and re-calibration are also discussed in this paper. Moreover, multi-innovation recursive least square algorithm is employed to update the unknown parameter vector for the model and improve the results. Our algorithm is tested based on the continuous data from MIMIC database, and the accuracy is calculated to validate the proposed method. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Since blood pressure carries a great deal of information about people’s physical attributes and usefully indicates cardiovascular diseases, the measurement/estimation of blood pressure has gained increasing attention. The approaches to measure blood pressure can be classified into invasive and non-invasive methods. Even though the invasive blood pressure measurement using an intra-arterial cannula can give continuous and accurate reading of beat-to-beat information, it also carries great risks and thus is restricted to a hospital setting in critical care [1]. Undoubtedly, the most widely used noninvasive blood pressure measurement techniques are auscultatory and oscillometry. The auscultatory method involves occlusion of arterial blood flow using an inflatable cuff such as a sphygmomanometer. Once the sphygmomanometer cuff is inflated until the artery is completely occluded, the stethoscope is then used to auscultate the Korotkoff sounds as the cuff is slowly deflated. The first Korotkoff sound can be heard when blood just starts to flow through the artery and the pressure at this point is accepted as the systolic pressure and as the cuff pressure is further released, no sound can be heard and this corresponds to the diastolic pressure. Because the auscultatory method is non-invasive, easy, and safe, it is well supported and has been accepted as ‘‘standard method’’ for clinical measurement despite the inaccuracy due to both observer errors and methodological errors. The oscillometric method also involves occlusion of blood flow using an inflatable cuff. However, rather than using a stethoscope, a calibrated electronic pressure transducer is used to monitor the pressure oscillations within the cuff. Since it is also non-invasive, easy, safe, and furthermore eliminates the mercury column, the oscillometry is used primarily in automated non-invasive blood pressure devices. Common limitations of measurement include the reliance on blood flow and selection of an inappropriate cuff size and position. More detailed descriptions of these techniques are readily available [1–3,5].

q

Reviews processed and proposed for publication to Editor-in-Chief by Associate Editor Dr. Anas Al-Rabadi.

⇑ Corresponding author.

E-mail address: [email protected] (S. Ko). 0045-7906/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compeleceng.2012.09.005

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In order to accommodate the rapidly growing need for home blood pressure monitoring system, not to mention the white-coat effect, non-invasive cuffless measurement methods are more commonly required for routine examinations and monitoring. Among studies focusing on the ways of blood pressure estimation based on other physiological parameters, pulse transit time (PTT) is widely accepted that it is closely related to arterial stiffness and can be used to estimate blood pressure [7,9,10]. PTT can be determined as the time delay from the R wave peak of electrocardiogram (ECG) to a characteristic point of finger photoplethysmogram (PPG) in the same cardiac cycle . The velocity of the pressure pulse traveling from aorta to peripheral arteries, which is the inverse of the PTT, was early recognized to be related to the elasticity of an artery [12,13]. An increase in blood pressure makes the arterial wall stiffer and increases the pulse wave velocity and therefore results in a deduction in PTT. It has been shown that PTT possibly can estimate the beat to beat systolic arterial blood pressure during or after the exercise [14,31] and is clinically applicable to respiratory sleep studies, cardiovascular studies, and small infants during critical care. The PTT technique, of course, has the advantages and disadvantages [22]; it is non-invasive, easy, safe, and no specialized training is required for medical staff to handle the ECG and PPG signals. Some inherent limitations of the PTT technique include a high influence of peripheral vascular resistance on the pulse waveforms and motional artefacts caused by the interference with peripheral PPG signal or ECG leads on the chest wall. Nonetheless, documented evidence available from the present literature suggests that PTT has the potential to be a surrogate marker for BP changes and used as a valuable clinical tool. Although most of PTT studies adapt advanced signal processing techniques, not many of them have fully considered the inherent nature of the non-linear and non-stationary properties of the measured ECG and PPG signals. The contributions of present work are originated from the effort to apply the signal processing technique that can handle this appropriately. New adaptive PTT-based blood pressure estimation using the Hilbert–Huang transform (HHT) is proposed and applied to various PTT measurement points to decide which reveals high correlation. And also different innovation length of recalibration for multi-innovation recursive least square algorithm is tested. In order to evaluate the performance, the continuous ECG, PPG and blood pressure waveforms openly available in MIMIC database are used. In the following, the HHT algorithm is introduced in Section 2. Section 3 illustrates the process of the PTT-based blood pressure method, including PTT calculation and blood pressure estimation model. In Section 4, the results are discussed to verify the effectiveness of the proposed algorithm. Discussion and Conclusion are presented in the last sections. 2. Data process Considering that the blood pressure inherits non-linearity and non-stationarity as its characteristics, the Hilbert–Huang transform (HHT) [15,16,4] is better technique to apply than other common transforms such as those based on Fourier transform. In the traditional Fourier analysis, the frequency is defined by using the sine and cosine functions spanning the entire length of data. Such a definition would not make sense for non-stationary data in which changes occur with time. This difficulty is overcome by the introduction of the approaches based on the Hilbert transform. 2.1. Hilbert–Huang transform Technically, HHT is a two-step algorithm combining empirical mode decomposition (EMD) and Hilbert transform. The complicated signal is decomposed into a finite number of intrinsic mode function (IMF) components since most time series involve more than one oscillatory mode and then the energy–frequency–time distribution of the decomposed data is created by the Hilbert spectral analysis. An IMF is a function having the same numbers of zero-crossing and extrema, and symmetric envelopes defined by the local maxima and minima respectively. All the data should be between the upper and lower envelope. Different from the Fourier transform based techniques, this method is not based on the simple harmonic components of constant amplitude and frequency, but instead it decomposes the signal into simple oscillatory modes of variable amplitude and frequency intrinsic oscillation along the time axis. As the first step, with a basic assumption the signal has at least two extrema, the EMD method is used to extract the IMFs and it is designated as a sifting process. The systematic sifting process is described as follows:  Step 1: Once all the extrema (maxima and minima) of the signal x(t) are identified, then a cubic spline line is used to connect the maxima to construct the upper envelope, emax(t); the lower envelope, emin(t), is obtained in the same way.  Step 2: Compute the mean of the upper and lower envelope, m(t) = [emax(t) + emin(t)]/2.  Step 3: Calculate the difference, d(t) = x(t)  m(t).  Step 4: Now take d(t) as the new signal and repeat the previous procedure until d(t) becomes a zero-mean process. This signal is designated as the first intrinsic mode component, c1.  Step 5: To make sure that the IMF components have enough physical sense in both amplitude and frequency, the standard deviation (SD) calculated from the two consecutive sifting results, is used as a criterion to stop the sifting procedure. The threshold is usually set SD to 0.2 or 0.3.  Step 6: Separate c1 from the original signal and the residual, r(t) = x(t)  c1, is used as the new signal. The sifting process is repeated to get the second intrinsic mode component.

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 Step 7: Repeat the above procedure to get all IMF components. This process can be stopped when the iteration reaches the predetermined value of substantial consequence or the residual is a monotonic function having only one minimum or one maximum. Assume that there are n IMF components and one residual, the original signal can be represented as:

xðtÞ ¼

n X cj þ r n :

ð1Þ

j¼1

The logic flowchart to decompose the input signal into successive IMFs is illustrated in Fig. 1. Once all IMF components are obtained, it is straightforward to apply the Hilbert transform to each component and get the instantaneous frequency, x. After conducting the Hilbert transform on each IMF component, the original data can now be expressed as the real part R in the following form:

" # R n X aj ðtÞei xj ðtÞdt :

xðtÞ ¼ R

ð2Þ

j¼1

where R is the real part of the complex number, aj(t) is a time-dependent expansion coefficient similar to the constant in the Fourier expansion and xj is the instantaneous frequency at a given time which differs from the constant frequency in Fourier transform. As mentioned previously, the HHT algorithm is a promising method to solve the non-linear and non-stationary data. Both ECG and PPG signals are inherently non-linear and non-stationary due to lots of interference, and the data are changing with time due to the physiological status [17]. So the HHT is employed to process the ECG and PPG signals to estimate the PTT. 2.2. Wavelet transform The wavelet analysis is a common tool for extracting information from data. Wavelet transform can be considered as a form of time-frequency representation [18]. It has gained extreme attention of some fields. Many applications are in edge detection and image analysis. However, wavelet analysis is non-adaptive, which means once the basic wavelet is selected, all the data have to be analyzed using it. Moreover, the leakage generated by the limited length of the basic wavelet function

Start EMD

Identify all extrema

Construct the upper and lower envelope

Compute the mean

Calculate the difference

IMF? No Yes Store IMF

Stop Criterion? No Yes End EMD

Fig. 1. Logic flowchart of the empirical mode decomposition (EMD).

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makes the quantitative definition of the energy–frequency–time distribution difficult. Despite these demerits, wavelet analysis still has been applied to analyzing the non-stationary data. Some researchers introduced it to the PTT-based blood pressure estimation [19–21]. So the wavelet transform is considered as a comparison in this work. 3. Algorithm description 3.1. Different PTT measurements The HHT algorithm is used to analyze the ECG and PPG signals. Since the definition of PTT is the time period from R wave peak of ECG to a characteristic point of PPG [23], the time instance of the data is of interest. The EMD method, the first step of the HHT algorithm, is applied to decompose the signal into the IMFs and then the Hilbert spectrum analysis is employed. Once data process is done, the rebuilt data are now subject to detect the characteristic points such as ECG QRS wave and PPG peaks/valleys. By original definition, PTT is the time interval between the R wave peak of ECG and a characteristic point of PPG in the same cardiac cycle. Many previous and ongoing blood pressure estimation researches based on PTT have studied the different PTT measurement points and revealed different results [11,25–27]. As shown in Fig. 2, three different measurement points of PTT are tested: peak, middle, or foot of PPG waveform defined as PTT-peak, PTT-middle, and PTT-foot, respectively. PTT-middle is the maximum derivative point. 3.2. Blood pressure estimation Even though it is consented that PTT is highly related to blood pressure [5,31,24,30,6,8], the BP estimation model based on PTT varies from linear to logarithmic to inverse of squares. Among different models used in the literature, the most common linear models of the form BP = a  PTT + b is adopted not because they provide a better performance, but because they have been observed to be more robust to the artifacts typically present in the non-invasive waveforms. Least square algorithm is employed to determine the unknown coefficients a and b, which is considered as the calibration process. As the blood pressure values are available in the database, the estimated blood pressure results by the PTT-based method can be compared with the actual blood pressure values. According to the Association for the Advancement of Medical Instrumentation (AAMI) requirements for BP estimation, the mean of the estimation error has to be lower than 5 mmHg in absolute value, and that the standard deviation of the error has to be below 8 mmHg, both for SBP and DBP. 3.2.1. Original calibration When the PTT values are detected, original calibration is performed firstly when the method is used for blood pressure estimation. About 40 measurement values of PTT are required for the original calibration for the acceptable outcome. Least square algorithm is a prevalent statistical method that has been widely employed in many applications. It minimizes the sum of the squares of the errors to achieve the proximal values. The original calibration in our work is accomplished through least square method. The procedure is stated as follows. The unknown coefficients a and b are gathered into the matrix

b¼

  a ; b

ð3Þ

for SBP and DBP respectively. We collect the blood pressure and PTT into matrices

2

3 BP1 6 . 7 7 Yn ¼ 6 4 .. 5; BPn

ð4Þ

R wave peak

ECG

PTT−peak PTT−middle

PPG

PTT−foot

Fig. 2. The definition of PTT.

Y. Choi et al. / Computers and Electrical Engineering 39 (2013) 103–111

2

3 1 .. 7 7 . 5; 1

PTT 1 6 . Xn ¼ 6 4 .. PTT n

107

ð5Þ

where n denotes the nth measurement. The coefficient matrix b is obtained from the minimization of kYn  Xnbk2:

h i1 bn ¼ X Tn X n X Tn Y n :

ð6Þ

e from Y e ¼ Xb, when the new measurements of PTT (X) are given. Whereafter, we can get the estimation of blood pressure Y 3.2.2. Comparison with wavelet transform processing technique To verify the acceptable results that we obtain using HHT, we compare it with the results of wavelet transform. By decomposing signals into elementary building blocks, the wavelet transform can extract ECG and PPG characteristic points [18]. We follow their method to filter the same data from MIMIC database, and use the same algorithm to initially calibrate the model. So the only effect on the different results is the two different signal processing techniques. 3.2.3. Periodic re-calibration The estimation performance of the PTT-based blood pressure method retains accurate within a certain period after the original calibration, so the periodic re-calibration is required. The periodic re-calibration should also follow the AAMI standards. One new measurement of SBP and DBP is used for the re-calibration. For the application, SBP and DBP can be measured by some cuff-based method. Recursive least square algorithm is employed to complete the re-calibration combined with the initial calibration (Eq. (6)). The new records of SBP, DBP, and PTT are given, then the new parameter matrix can be calculated from the minimization of kYn+1  Xn+1bk2:

bnþ1 ¼ bn þ Lnþ1 ðynþ1  xnþ1 bn Þ; h i1 h i1 Lnþ1 ¼ k1 X Tn X n xTnþ1 ð1 þ k1 xnþ1 X Tn X n xTnþ1 Þ1 ;

ð8Þ

ynþ1 ¼ ½ BPnþ1 ;

ð9Þ

ð7Þ

where

xnþ1 ¼ ½ PPT nþ1

1 ;

ð10Þ

and typical range of the forgetting factor k is 0  k 6 1. In our experiments we use k = 0.95. By extending the conventional recursive least square algorithm, the multi-innovation technique is introduced for linear regression models with unknown parameter vectors [28,29]. Since the multi-innovation recursive least square algorithm uses more than one innovations, the accuracy of the parameter estimation is improved compared with the standard recursive least square algorithm. In order to improve the accuracy, a number of innovations are well used to obtain the parameter vector. The scalar innovations yn+1 and xn+1 in Eq. (7) are expanded to the innovation vectors yn+1(p,t) and xn+1(p,t):

2

BP nþ1

3

7 6 6 BPn 7 7 6 7 6 ynþ1 ðp; tÞ ¼ 6 BP n1 7; 7 6 . 7 6 .. 5 4 BPnpþ1 2

PPT nþ1 6 6 PPT n 6 6 xnþ1 ðp; tÞ ¼ 6 PPT n1 6 .. 6 . 4 PPT npþ1

ð11Þ

1

3

7 17 7 17 7; .. 7 7 .5 1

ð12Þ

where p represents the innovation length. The accuracy of the multi-innovation recursive least square algorithm can be improved by using not only current data point but also the past data points recursively for each iteration. In our application, besides the new measurement of SBP, DBP and PTT, some innovations from the first 40 measurements for initial calibration are also used for the re-calibration. Different lengths of innovation need to be discussed to choose the better one.

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Y. Choi et al. / Computers and Electrical Engineering 39 (2013) 103–111 Table 1 Error mean (mean), error standard deviation (SD) and correlation coefficient (r) for different PTTs averaged over all 33 records; SBP (top) and DBP (bottom). PTT-peak

PTT-foot

PTT-middle

SBP

Mean (mmHg) SD (mmHg) r

0.44 3.85 0.71

0.63 5.49 0.09

0.47 5.28 0.32

DBP

Mean (mmHg) SD (mmHg) r

0.93 1.84 0.69

1.05 2.67 0.02

0.96 2.67 0.22

Record 430 200 measured SBP estimated SBP measured DBP estimated DBP

180

Blood Pressure (mmHg)

160 140 120 100 80 60 40 20

50

100

150

200

250

300

350

Time (sec) Fig. 3. Measured and estimated SBP and DBP for record 430.

Table 2 Error mean (mean), error standard deviation (SD) and correlation coefficient (r) of wavelet transform (WT) and HHT.

Mean (mmHg) SD (mmHg) r

SBP HHT

SBP WT

DBP HHT

DBP WT

0.44 3.85 0.71

0.49 4.17 0.55

0.93 1.84 0.69

0.87 2.57 0.48

4. Results The algorithm as stated above is applied to the data carefully chosen from MIMIC database; out of 72 records, 33 records from 25 different patients have complete ECG, PPG and blood pressure which are good enough to detect their peaks and valleys. Table 1 shows mean, standard deviation of the SBP and DBP estimation errors, averaged over all 33 25-min-long records along with correlation coefficient for various PTT definitions. Shown results are obtained from linear model blood pressure estimation. It is clear that blood pressure estimated using PTT-peak has higher correlation coefficient and smaller error. Fig. 3 shows the measured and estimated blood pressure waveforms for a portion of record 430. Both SBP and DBP waveforms are in close agreement and the estimated waveforms follow the trends of the measured waveforms. Wavelet transform has been applied for comparison with HHT, both using PTT-peak. The result in Table 2 shows that Hilbert–Huang transform has better performance in error mean, error standard deviation, and even correlation coefficient for SBP. However, when it comes to DBP, wavelet transform results in slightly better error mean. As briefly mentioned before, 40 measurements of SBP and DBP are used for the initial calibration stage. In order to evaluate the effect of the calibration period on the BP estimation, different time periods of 30-min, 60-min and 90-min re-calibration are tested and the results are shown in Table 3. In general, longer re-calibration period will improve the practicability while shorter period will increase the accuracy. Considering the mean and standard deviation of the estimation error and also amenability of the system, we re-calibrate every 1 h. To improve the re-calibration performance, the multiinnovation algorithm is employed to the standard recursive least square. Fig. 4 shows the results of error mean and error standard deviation with different innovation lengths. The p indicates the innovation length, while p = 1 represents the standard recursive least square. It can be seen that the absolute value of error mean gives the best result at p = 1, while the best standard deviation occurs at p = 5. The results after re-calibration using standard recursive least square and

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Y. Choi et al. / Computers and Electrical Engineering 39 (2013) 103–111 Table 3 Error mean (mean) and error standard deviation (SD) for different time periods. All in mmHg.

30-min 60-min 90-min

SBP mean

SBP SD

DBP mean

DBP SD

0.81 0.63 1.77

5.48 5.98 8.15

0.34 0.19 1.22

2.94 3.21 4.85

−1

9

SBP DBP

SBP DBP 8

Error SD (mmHg)

Error mean (mmHg)

−1.5 +

−2

−2.5

−3 +

−3.5

7 +

6

5

4

−4

+

3 1

2

3

4

5

6

7

8

9

10

1

Innovation length, p

2

3

4

5

6

7

8

9

10

Innovation length, p

Fig. 4. Error mean and error standard deviation for different innovation length.

Table 4 Error mean (mean) and error standard deviation (SD) for re-calibration with multi-innovation recursive least square p = 5 (RE-MI) and standard recursive least square p = 1 (RE).

Mean (mmHg) SD (mmHg)

SBP RE-MI

SBP RE

DBP RE-MI

DBP RE

3.13 6.28

1.77 6.70

1.93 3.85

1.02 4.10

multi-innovation recursive least square are shown in Table 4. The multi-innovation algorithm improves the error standard deviation, which is a main consideration in the PTT-based blood pressure estimation method.

5. Discussion Our results found strong correlations of PTT to SBP and DBP and suggest that the proposed method can be a potential noninvasive and cuffless blood pressure measurement. However, with respect to different PTT methods, it is not in agreement with [25] in which PTT-middle is the best in terms of minimizing the error standard deviations. Many of the presented PTT based blood pressure estimation researches are not tested against the same data set. In order to validate different algorithms, reproducible evaluation on a standard database may be necessary. The validity of applying the simple first order equation of BP = a  PTT + b can be a topic of discussion. Even though we understand that the beat-to-beat blood pressure should be estimated as accurate as possible by considering all the affecting factors such as heart rate, contractility, cardiac output, peripheral resistance and cardiac preload, but we also should consider complexity of the model and its performance. Also it has been reported that the linear model is more robust to the artifacts typically present in the non-invasive waveforms. Another research done to the same MIMIC data [25] observed that BP is highly correlated with not only PTT but also instantaneous heart rate (HR) and used BP = a  PTT + b  HR + c as their model. We observe that BP measurement based on PTT using HTT outperforms two-parameter-model in terms of minimizing the error standard deviation (3.85 mmHg vs. 7.77 mmHg). Limitations of this study include that valuable MIMIC data is collected from ICU patients which implies, in some cases, the range of BP variation is not enough unlikely from the studies using direct drug administration. And also the age of subjects is very high at an average of 67.96 years. It is clear that aging affects arterial stiffness which directly impacts the PTT and inadvertently distorts the BP estimation. Also, it should be noted that the practicality of the model is retained by ignoring a few parameters such as the elasticity of arterial wall, pre-ejection period (PEP) because we cannot non-invasively measure them. PEP is a small delay between the R wave peak marking the electric excitation of the heart contraction and mechanical cardiac contraction. As this delay is negligibly short compared to the actual PTT, the PTT usually considered to include the PEP.

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However, for subjects with low heart rate, the PEP is speculated that it may become more significant. To accommodate this issue, the model may have to estimate the PEP as well or a PEP-free PTT derived by using a two PPG setup along the arterial passage. 6. Conclusion This study explored a potential cuffless non-invasive blood pressure estimation method based on pulse transit time (PTT) between electrocardiogram (ECG) and photoplethysmogram (PPG) using signal processing techniques. Hilbert–Huang transform (HHT) algorithm was applied to analyze both ECG and PPG data prior to obtaining the PTT values with various measuring points such as PTT-peak, PTT-foot, and PTT-middle. Based upon the derived PTT values, PTT-peak showed better performance with all respects to error mean, error standard deviation, and correlation coefficient (SBP: mean = 0.44 mmHg, SD = 3.85 mmHg, r = 0.71, DBP: mean = 0.93 mmHg, SD = 1.84 mmHg, r = 0.69). The blood pressure and PTTs were found inversely correlated except DBP and PTT-foot. The model to estimate blood pressure was calibrated and re-calibrated after 1 h and multi-innovation recursive least square method with the innovation length of p = 5 outperformed standard recursive least square and reduces the error standard deviation (SBP: 6.28 vs. 6.70 mmHg, DBP: 3.85 vs. 4.10 mmHg). The ultimate goal is to implement an wearable system to calculate PTT from an embedded ECG collector and a ring-shape pulse oximeter. This would allow PTT to be a widely available measurement tool, which is non-invasive, easy to use, and a safe method of continuously monitoring blood pressure. Acknowledgment This work is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under Strategic Project Grant Number STPGP 407458. References [1] [2] [3] [4] [5] [6] [7]

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[27] Deb S, Nanda C, Goswami D, Mukhopadhyay J, Chakrabarti S. Cuff-less estimation of blood pressure using pulse transit time and pre-ejection period. In: International conference on convergence information technology; 2007. p. 941–44. [28] Ding F, Chen T. Performance analysis of multi-innovation gradient type identification methods. Automatica 2007;43:1–14. [29] Zhang J, Ding F, Shi Y. Self-tuning control algorithms based on multi-innovation stochastic gradient parameter estimation. Syst Control Lett 2009;58(1):69–75. [30] Poon C, Zhang Y. Cuff-less and non-invasive measurement of arterial blood pressure by pulse transit time. In: Proceeding of the IEEE engineering in medicine and biology 27th annual conference, Shanghai, China; 2005. p. 5877–80. [31] Teng X, Zhang Y. An evaluation of a PTT-based method for noninvasive and cuffless estimation of arterial blood pressure. In: Annual international conference of the IEEE engineering in medicine and biology society, New York City, USA; August 2006. p. 6049–52. Younhee Choi received her Ph.D. degree in biomedical engineering at the University of Rhode Island, USA in 2006. She briefly held the postdoctoral position at the University of Saskatchewan before joining the faculty at the University of Saskatchewan as a lecturer. Currently she is serving as a research associate and her research interests include mathematical cardiopulmonary modeling, fall and near-fall detection device and algorithm development, assessment of cardiac performance with digital ballistocardiograph, biosensor technologies. Qiao Zhang received her Bachelor degree in Biomedical Engineering from Beijing Institute of Technology in 2008 and Master degree in Biomedical Engineering from University of Saskatchewan in 2010. Now she is working at Glenrose Rehabilitation Research Centre in Edmonton. Her main research interests include signal processing, physiology parameters, and scoliosis. Seokbum Ko received his Ph.D. in Electrical and Computer Engineering at the University of Rhode Island, Kingston, Rhode Island, USA in 2002. He is currently an associate professor in the department of Electrical and Computer Engineering at the University of Saskatchewan, Saskatoon, Canada. He worked as a member of technical staff for Korea Telecom Research and Development Group, Korea from 1993 to 1998. His research interests include computer arithmetic, digital design automation, and computer architecture. Dr. Ko is a senior member of IEEE computer society.