Blood Pressure Estimation using Built-in Sensors ofa Smart Device

Gung-Yu Pan∗†, Dao Lam†, Patrick Hsu†, Albert Xu†, Chung-Kuan Cheng†, Bo-Cheng Charles Lai∗, and Jing-Yang Jou‡∗∗Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu, Taiwan

†Department of Computer Science and Engineering, University of California, San Diego‡Department of Electrical Engineering, National Central University, Taoyuan, Taiwan

Email: {g1pan, d2lam, pthsu, a8xu, ckcheng}@ucsd.edu, bclai@mail.nctu.edu.tw, jyjou@ee.ncu.edu.tw

Abstract—As smart devices achieve tremendous popularity inmodern society, it is desirable to obtain blood pressure valueswithout cuff-based sphygmomanometer for daily monitoring. Inthis paper, a noninvasive blood pressure estimation approach isproposed using single smart device with built-in microphone andcamera. The heart period, the left ventricle ejection time, andthe pulse transition time are extracted from noisy signals usinglearning-based signal processing algorithms. At last, regressionequations are derived for the systolic (R = 0.686) and diastolic(R = 0.537) blood pressure values.

I. INTRODUCTION

Blood pressure (BP) is a vital indicator in early diagnos-ing cardiac vascular diseases, such as hypertension, diabetes,and kidney failure [1]. Not only do patients need long-termmonitoring, but it is also advantageous to pose an earlywarning for normal subjects. Among noninvasive blood pres-sure measurement methods, cuff-based sphygmomanometer isthe most widely-used equipment. Basic training is requiredin order to measure blood pressure; besides, the process ofclamping the limb discomforts the subjects. Therefore, severalalternative techniques are developed by estimating blood pres-sure from other measured biomedical signals, such as electro-cardiography (ECG), photo-plethysmography (PPG), phono-cardiography (PCG) [2][3], and so on.

As smart devices become widely-equipped recently whilethe computation capability keeps advancing, daily monitoringbecomes possible. Wearable devices with specific sensors areable to record the above signals, while our goal is developing alow-cost BP estimation methodology using smart phones andpads so that the users can monitor BP by recording the pulsesound and video using the built-in microphone and camera.Furthermore, the signals are recorded on the neck and thewrist, which are more convenient for the users to acquire thanheart signals. After signal processing, principal features areextracted to estimate both systolic blood pressure (SBP) anddiastolic blood pressure (DBP) through regression or learning-based approaches [4][5].

Pulse transition time (PTT) is one of the importantfeatures in BP estimation. Since PTT requires two refer-ence points, two separation sensors are needed, such asmicro-electromechanical sensors [2], microphones of two syn-chronous smart phones [3], external microphones [3], specificsensors using ECG, PPG, and so on [1]. On the other hand,PTT can hardly be derived using one serial recording from

Radial

Pulse

Carotid

Pulse

Aortic

Pulse

Carotid

Sound

Heart

Sound

HP

LVET

PTT

Fig. 1. The signals and the features.

two different sites due to the natural heart rate variation(HRV). Video provides an alternative that can capture two sitessimultaneously. The pulse signals can be extracted from thesubtle movement or intensity change of the skin [6][7].

Heart period (HP) and left ventricular ejection time (LVET)are other two important features in estimating BP where thevalues can be extracted from carotid sound in high precision[8]. Then, learning-based algorithms are designed to analyzenoisy signals with natural HRV.

In our proposed approach, three biomedical signals areacquired as depicted in Fig. 1: the carotid pulse sound arerecorded by the microphone, and the carotid and radial pulsewaves are captured as video by the camera. After proper signalprocessing methods, HR and LVET values are derived fromthe carotid sound waveform while PTT is obtained from thevideo including carotid and radial pulses. At last, SBP andDBP values are deduced by regression. To the best of ourknowledge, this is the first approach to estimate the bloodpressure using single bare smart devices. In short, the proposedmethod has the following advantages:

• The blood pressure can be estimated using a singlesmart device.

• Neither hardware overhead nor specialized biomedicalsensors are required.

• Generalized feature extraction algorithms are proposedto process noisy biomedical signals.

The remainder of this paper is organized as follows. Theproposed method is illustrated in Section II, and then evaluatedin Section III. Finally, this paper is summarized in Section IV.

II. THE PROPOSED METHOD

A. Overview

There are three main steps in the proposed approach: signalacquisition, feature extraction, and blood pressure estimation.

First, the audio and video signals are recorded by the users.The built-in microphone is put on the neck to record the carotidpulse sound, and then the wrist is raised closely to the neck tocapture the carotid and the radial pulses simultaneously. Theprocess is completed in approximately 30 seconds.

Then, the acquired signals are processed separately toextract the features. The first and the second carotid pulsesounds (CaS1 and CaS2) are detected on the audio waveform.The length between consequent CaS1 peaks is the HP, and thelength between CaS1 and CaS2 peaks is the LVET. The peaksbetween the carotid and radial pulse waves is the PTT.

Afterward, the SBP and DBP values are derived usingthe above features. In addition, the body surface area (BSA)calculated using height and weight values is taken to representpersonal characteristics.

The details of audio processing, video processing, andpressure estimation are described in the following subsections.

B. Audio Signal Processing

There are three steps to obtain HP and LVET values fromthe carotid pulse sound: preprocessing, peak detection, andsegmentation.

The waveform first undergoes a low-pass filter with a cut-off frequency of 300Hz to remove high-frequency noise, andnormalized to zero mean and unit maximum absolute peak.Then, its envelope is detected using Shannon energy due to itsgood performance and generality [9]

y[n] = − 1

2N + 1

n+N∑k=n−N

x2[k] log x2[k] (1)

where x(t) and y(t) are the input and output waveforms,respectively, and 2N +1 is the window size; 20ms is taken inour implementation. In order to ignore large-amplitude noisypeaks and enlarge signal details, especially for the indistinctcarotid first sound (CaS1) peaks, the envelope is then processedin normalized logarithm scale.

The approximate heart rate is obtain using the autocorre-lation function on the envelope as

yc[n] =∑k

y[k] · y[k − n]. (2)

The largest peak within range 250–1500 ms (heart rate between40 to 240 beats per minute) is taken as the approximate periodT for segmentation usage (the precise HP is extracted later).

Then, a low-threshold peak detection function is applied tothe envelope, while many false peaks should be eliminated. Allof the peaks are clustered into two groups according to theirmodulo distance. The normalized modulo distance betweenpeak position pi and pj is defined as

dij =1

Tmin

{(pi − pj) mod T(pj − pi) mod T

(3)

Fig. 2. False peaks elimination and segmentation.

where the modulo T is the approximate period. In orderto provide fast convergence rate and reject outliers, the K-means++ clustering algorithm [10] is chosen to classify all thepeaks into two types. In addition to correct peaks, there aresome neighboring false peaks due to noise, which should beeliminated. For a sequence of peaks with the same type, onlyone peak is selected, according to the height, the prominence,and the location of each peak. The location of the ith peak isscored as

si =

∑typei=typej

e−dij∑i

∑typei=typej

e−dij. (4)

An example of the peak detection algorithm is depicted inFig. 2. In the upper-left subplot, many false peaks are createddue to the low threshold, while their scores shown in the redline is lower than adjacent correct peaks. The location of eachpeak on the modulo domain is depicted in the right subplot;false peaks (red crosses) are usually randomly distributed dueto noises. The correct peaks are shown in lower-left wheresome higher peaks are considered false in our algorithm dueto incorrect location.

The last step is labeling the two types into CaS1 andCaS2. Since the systolic time is shorter than the diastolic timeon average, the types can be determined by comparing thedistances between adjacent peaks. The HP value is averagedfrom adjacent CaS2 peaks, because the second sound is easierto detect and more reliable than the first sound on the carotidartery. The LVET value is averaged from the distance betweenCaS1 and CaS2 peaks, corresponding to the opening and theclosing of the aortic valve, respectively [8].

C. Video Signal Processing

There are four steps to obtain PTT from the video con-taining carotid and radial pulses: feature tracking, principalcomponent analysis, peak detection, and segmentation.

The subtle movement of several feature points on the neckand the wrist are tracked using the Kanade-Lucas-Tomasi algo-rithm [11]. Then, the signals are applied with a bandpass filterwithin [0.75 5] Hz to suppress noises as in [6]. Because eachof the feature points have horizontal and vertical components,they are combined as the movement in Euclidean distance.

The movement of the skin on either neck or wrist maycome from other sources other than pulses, such as respirationand motion artifacts. Therefore, up to 25 feature points aretracked simultaneously on either neck or wrist, and then thepulse waves are extracted by Principal Component Analysis

(a) The carotid (neck) pulse.

(b) The radial (wrist) pulse.

Fig. 3. Example pulse waves after PCA.

(PCA) [6]. Some feature points aside the pulses are trackedas well, so the artifacts can be separated by PCA. The targetpulse is selected by comparing the signal period calculated bythe autocorrelation as in (2) with the HP extracted from thesound signal. The example pulses are shown in Fig. 3; thecarotid pulse is contaminated during 1900ms and 7800ms, sothe corresponding PTT values are dropped by range filtering.

Similar to the audio processing method, the peaks aredetected using a low threshold, and then the false peaks areeliminated. Dissimilar to pulse sounds, only the systolic peak(normally the highest one in each period) is interested. Themodule distance of each pair of peaks is calculated as in (3)where the double period 2T is taken as the module to separateadjacent peaks. Each peak is scored using weighted sum ofheight, prominence, and modulo location. The location scoreis similar to (4) without the type check, while only one peakis chosen in each period. The PTT values are calculated bysubtracting adjacent peak locations of carotid and radial pulses.

D. Parameter Deduction

The real blood pressure values are determined throughcomplicated mechanisms [1], and related to plenty of biomed-ical signals. Among them, PTT and LVET are proven to havestrong correlation with blood pressure [12][4].

According to the Moens-Korteweg equation [1], PTT isinversely proportional to the square root of arterial stiffness,which is positively correlated to blood pressure. The increasein arterial stiffness causes the increase in BP and pulse wavevelocity (PWV), hence the decrease in PTT.

LVET is mainly related to the pulse pressure (PP), whichis the difference between SBP and DBP. For the same strokevolume (SV), which the amount of blood pumped from theheart every period, larger PP corresponds to shorter LVET.

In addition, HP is included to provide normalization forthe different states of a subject; the higher heart rate shortensLVET, and HP is taken for regression with PTT in previousstudies [13]. BSA calculated from height and weight is taken torepresent personal characteristics; larger BSA requires higherSV and taller subjects have longer PTT.

TABLE I. SUBJECT CHARACTERISTICS

Age Height Weight SBP DBP HR(year) (cm) (kg) (mmHg) (mmHg) (bpm)

Average 33.9 173.4 71.0 110.5 72.3 71.7Standard Deviation 13.2 9.5 14.5 12.3 9.0 10.1

TABLE II. CORRELATION COEFFICIENT BETWEEN FEATURES ANDBLOOD PRESSURE VALUES

HP LVET PTT BSASBP -0.022 -0.321 -0.320 0.709DBP 0.036 -0.205 -0.320 0.597MAP 0.016 -0.247 -0.324 0.640

PP -0.176 -0.377 -0.023 0.606

Fig. 4. The relation between LVET and PP.

Fig. 5. The relation between PTT and MAP.

III. RESULTS AND DISCUSSIONS

A. Environment

Ten healthy subjects (seven males and three females)are measured where the characteristics are listed in Table I.Data samples are recorded for up to 15 seconds in a quietenvironment. Each subject is in resting condition, and sittingstill with one hand positioned next to the neck. Videos arecaptured in 30 fps, and audios are recorded in 8 KHz samplingrate. The reference blood pressure is measured by a wrist-type blood pressure monitor; the values are averaged from themeasurements before and after recording.

B. Correlation

The correlation coefficient between input features (ex-tracted and characteristic) and blood pressure values (measuredand derived) are shown in Table II.

LVET is negatively proportional to the blood pressurevalues, especially for PP as shown in Fig. 4. PTT is negativelycorrelated to the blood pressure as shown in Fig. 5, butindependent to PP, which is not shown here. Therefore, bothfeatures are necessary to span the estimation of SBP and DBP.

Different subjects are represented by different symbols.Although the personal characteristics are captured by BSA,different subjects still have divergent behaviors. After morepopulation are measured, other features can be taken as input,such as age and gender.

Fig. 6. Blood pressure estimation versus measurement.

Fig. 7. HP and LVET variations.

C. Estimation

The linear regression results are shown in Fig. 6 wherethe vertical and horizontal axes represent the measured andestimated blood pressure, respectively. The regression ratio is0.686 for SBP and 0.537 for DBP.

The ranges of extracted features and trends of correlationare consistent with previous works [8][12], while the accuracyand regression ratio are lower, especially for PTT. It is believedthat future smart devices equipped with better cameras are ableto acquire videos with higher resolution in time.

Since the real blood pressure varies, the measured valuesonly represent one specific time, and compared with theaveraged values of the features (HP, LVET, and PTT). Twoexample samples are shown in Fig. 7; the LVET valuesare relatively stable while lightly varying with HP. A betterrelationship can be obtained by using a continuous-time bloodpressure measurement device while recording the audio andvideo samples.

Currently, subjects are required to sit still while the deviceis fixed on a tripod to avoid large motion artifacts, which can-not be entirely removed by filtering and PCA. Advanced imagestabilization techniques are combined, so that the users canhand-hold the device. Intensity signals (PPG) may be obtainedin addition to feature tracking to provide an alternative forextracting pulse wave [7]. When measurements are extendedto patients with heart and vascular diseases, signal processingis more challenging to handle divergent waveforms such as theabsence of carotid first sound or murmur [14]. Furthermore,linear regression may become inaccurate when the ranges ofextracted features are enlarged in divergent conditions (such asaerobic exercise); nonlinear regression or even neural networksare exploited to find the relations between features in thefuture.

IV. CONCLUSIONS

In this paper, a noninvasive blood pressure estimationapproach is proposed. Different from existing works whichexploit external sensors, only a built-in microphone and acamera of a smart device are required in our method. Principalbiomedical features are extracted from audio and video signalsby delay-based segmentation to estimate blood pressure values.The results are consistent with previous works using externalsensors, demonstrating the feasibility of this work.

The proposed method can be implemented as an Appfor smart devices, such that blood pressure values can beconveniently monitored by users on a daily basis. Since pulsewaves and sounds are measured, some other features such astime to reflection (Tr) and augmentation index (AIx) can alsobe extracted to provide more insight information. In the future,the proposed approach can be combined with other symptomdetection techniques to enhance human life.

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