Continuous Cuffless Blood Pressure Estimation with ...users.ox.ac.uk/~engs1850/PhD_oral_defense_XiaorongDing.pdf · (n=48) Mean age (range) 57 (18-96) 27 (22-54) 80 (78-96) Gender

香港中文大學The Chinese University of Hong Kong

Continuous Cuffless Blood Pressure Estimation with Respiratory-modulated Pulse Transit Time

and Photoplethysmogram Intensity Ratio

Xiaorong DING

Supervisor: Prof. Hon Ki Tsang

Co-supervisor: Prof. Yuan-Ting Zhang

Department of Electronic Engineering

June 24, 2016

PhD Oral Defense

Outline

Introduction

Effect of Cardiovascular Disease and Calibration

Interval on Blood Pressure Estimation

Pulse Transit Time for Respiratory Rate Estimation

Cuffless BP Estimation with Pulse Transit Time and

Photoplethysmogram Intensity Ratio

Conclusions and Suggestions for Future Work

1

1

1

1

Background

BP Measurement Techniques

PTT Method for Cuffless BP

Research Problems

Study Objectives

1Introduction



Pulse Transit Time for Respiratory Rate

Estimation

BP Estimation with Pulse Transit Time and



Analysis PTT for RR PIR and PTT for BPIntroduction Conclusion and Future Work

•Cardiovascular diseases (CVDs) - No. 1 Killer

Background

CVDs

Global Atlas on Cardiovascular Disease Prevention and Control. WHO (2011).

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Hypertension

Chow, et al. JAMA (2013).

•Blood pressure (BP) variability and continuous BP

- Early detection and prevention of hypertension

•Accurate measurement of BP

- Diagnose, evaluate, management

47% 33%40%

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Hypertension


BP Measurement Techniques

Ding, Liu, et al., IEEE Life Sciences Newsletter (2014).

Noninvasive

Invasive

Intrusive Unobtrusive

Intra-arterial

Auscultatory

Oscillometric

TomometryVolume clamp

Cuffless BP monitor

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Intermittent

Continuous Beat-to-beat/continuous

Implantable


•Pulse transit time (PTT)

•Pulse wave velocity (PWV) recording- Moens-Korteweg (M-K) equation:

Principle of PTT-based BP Measurement

𝐏 =1

γlnρL2

E0+ lnd − lnh − 2ln𝐏𝐓𝐓

L: pulse travelling distanceE: elastic modulush: blood vessel thicknessd: arterial diameter𝝆: blood densityγ: relevant to measurement siteE0: zero-pressure modulusP: blood pressure

Electrocardiogram (ECG)

Photoplethysmogram (PPG)

R Wave

PTT

𝐏𝐖𝐕 =𝐄𝐡

𝛒𝐝

𝐄 = 𝐄𝟎𝐞𝛄𝐏

Moens, A. and Korteweg, D. J., et al, (1878). Hughes et al. (1979). Bramwell JC, and Hill AV, (1922).

L

𝐏𝐖𝐕 =𝐋

𝐏𝐓𝐓

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𝐏𝐖𝐕 =𝐕

𝛒∆ 𝐕 ∆𝐏B-H equation:


Author Method (Model)

Calibration &

Reference

Method

Subjects SBP DBP

Error (mmHg) Correlation Error (mmHg) Correlation

Young et

al (1995)

SBP=a1/PTT+b1

DBP=a2/PTT+b2

Oscillometric

BP35 patients

-0.37 (-

29.0~28.2)

-0.01 (-

14.9~14.8)

Chen

et al

(2000)

𝐒𝐁𝐏 = 𝐒𝐁𝐏𝟎 −𝟐

𝛄𝐏𝐓𝐓𝟎𝐏𝐓𝐓 − 𝐏𝐓𝐓𝟎

Intermittent

BP from

Invasive BP (5

min)

20 patientsRMSE:

3.70 ± 1.850.97±0.02

Poon

et al

(2005)

𝐃𝐁𝐏 = 𝐌𝐁𝐏𝟎 +𝟐

𝛄𝐥𝐧𝐏𝐓𝐓𝟎

𝐏𝐓𝐓−𝟏

𝟑𝐏𝐏𝟎 ∙

𝐏𝐓𝐓𝟎

𝐏𝐓𝐓

𝟐

𝐒𝐁𝐏 = 𝐃𝐁𝐏 + 𝐏𝐏𝟎 ∙𝐏𝐓𝐓𝟎

𝐏𝐓𝐓

𝟐

Cuff BP

(Initial

calibration)

85 (39

hypertensive

s)

Mean±SD:

0.6±9.8

Mean±SD:

0.9±5.6

Douniama

et al

(2009)

SBP=a1*PTT+b1

DBP=a2*PTT+b2Invasive BP

14 ICU

patientsSD: 6.73 SD: 5.28

Muehlsteff

et al

(2006)

SBP=a*lnPTT+bCuff BP

(Physical test)18 healthy RMSE: 7.5

Masè

et al

(2011)

SBP=a1/PTT+b1

DBP=a2/PTT+b2

Cuff

sphygmomano

meter

(exercise) / 1

month

33 healthy-0.06

95% CI: -

13.0~+12.9

0.94

-0.25

95% CI: -

11.3~+10.8

0.88

Muehlstef

f et al

(2006)

SBP=a*(L/PTT)2+bCuff BP

(Physical test)18 healthy

RMSE: 6.9

RMSE: 7.30.93

Previous Study

Young, et al. Journal of Clinical Monitoring (1995).

Chen, et al. Medical & Biological Engineering & Computing (2000).

Poon and Zhang. EMBC’05 (2005).

Douniama, et al. Computer in Cardiology (2009).

Muehlsteff, et al. EMBC’06 (2006).

Mase, et al. Journal of Electrocardiology (2011).

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Standards Reference Device Sample

Size

Difference between standard and

test device (mmHg)

Recommendation

for clinical use

AAMI Mean Standard Deviation (SD)

Mercury sphygmomanometer ≥85 ≤5 ≤ 8 Yes

Intra-arterial BP ≥15 ≤5 ≤ 8 Yes

BHS Grade Absolute difference

≤5 ≤10 ≤15

Mercury sphygmomanometer ≥85

A 60% 85% 95% Yes

B 50% 75% 90% Yes

C 40% 65% 85% No

D Worse than C No

ESH Grade Absolute difference

Mercury sphygmomanometer 33

≤5 ≤10 ≤15

1 73% 87% 96% Yes

2/3≤5 0/3≤5 -

2 ≥24% ≤3% Yes

3 - - - No

IEEE 1708 Grade Mean absolute difference (MAD)

Mercury sphygmomanometer 45

A ≤5 Yes

B 5-6 Yes

C 6-7 Yes

D ≥7 No

(2002)

(1993)

(2010)

(2014)

AAMI, American National Standard (2002). BHS protocol (1993).

European Society of Hypertension International Protocol (2010).

IEEE Standard for Wearable, Cuffless BP Measuring Device (2014).

Brief summary of the major works on PTT study for BP estimation.


•BP is a hemodynamic parameter

•Unobtrusive way to address various factors and measure BP with acceptable accuracy

Challenge

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Systolic (SBP)

Diastolic (DBP)

Mean (MBP)

Pulse Pressure (PP)

Determinants of arterial BP


Problem Statement

Potential factors

• Cardiovascular disease

• Calibrate interval

• Destroy assumptions

PTT

• PTT – high frequency

variation (Ma et al; Liu

et al)

• Dynamic BP – high

frequency + low frequency

• Sole PTT

• Regression models

• Cardiovascular system

is complex

PTT-BP models


𝛒𝐝

PTT ~ f(HF)

𝐁𝐏 = 𝐟 𝐋𝐅 + 𝐟 𝐇𝐅

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Cardiovascular

System

PTTBP

?

Accuracy

Ma, et al. EMBC’06 (2006); Liu, et al. Biomedical Signal Processing and Control (2011).


•To analyze the potential factor affecting the accuracy

- Cardiovascular disease

- Calibration interval

•To research PTT and respiration

- Whether PTT reflect the respiratory-induced variation BP

- PTT for respiratory rate measurement

•To improve BP estimation accuracy

- Propose a new indicator

- Establish new BP models

Purpose of the Study

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1

1

1

1

Motivation and Objective

Methodology

Accuracy of Healthy and Patients Group

Accuracy of Different Calibration Intervals

Conclusions

1

Introduction




Estimation





•Simplifying assumption

- Thin, pure elastic artery E

- No change h, d

•Physiologically

- E alters with age and

cardiovascular disorders

- d changes during each cardiac cycle

- h would change with disorder

•Aim

- Impact of cardiovascular disease

- Impact of calibration interval

Problems of PTT Study with M-K Equation

𝑳

𝑷𝑻𝑻=𝑬𝒉𝝆𝒅

E: elastic modulush: blood vessel thicknessd: arterial diameter𝝆: blood densityP: blood pressureL: pulse travelling distance

https://cnx.org/contents/A4QcTJ6a@3/Blood-Flow-Blood-Pressure-and-

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•PTT-BP model

•Experimental data

Approach

DBP = MBP0 +2

γlnPTT0PTT−1

3PP0 ∙

PTT0PTT

2

SBP = DBP + PP0 ∙PTT0PTT

2

Poon and Zhang. EMBC’05 (2005).

Subjects

(n=85)

Health Group

(n=37)

Patient Group

(n=48)

Mean age (range) 57 (18-96) 27 (22-54) 80 (78-96)

Gender (M/F, n) 37/48 21/16 16/32

Hypertension (n) 36 0 36

Congestive heart failure (n) 9 0 9

Atrial fibrillation (n) 5 0 5

Ischemia heart disease (n) 5 0 5

MBP0, PP0: calibrated MBP and PP

PTT0: calibrated PTT

PTT: calculated PTT

SBP, DBP: estimated BP

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•Data collection

•Data analysis

- Mean ± SD, mean absolute difference (MAD)

- Bland-Atman plot, limit of agreement mean±1.96*SD

Data Collection and Analysis

ECG, PPG; cuff-BP

PTT for BP estimation

Reference BP

Calibrated BP

Calibrated PTTDay 1

Week 1

Week 2

Week 6

ECG, PPG

cuff-BP

PTT calculation:

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Accuracy: Healthy versus Patient

Overall Healthy Group Patient GroupMean±SD

(mmHg)

MAD

(mmHg)

Mean±SD

(mmHg)

MAD

(mmHg)

Mean±SD

(mmHg)

MAD

(mmHg)

SBP 1.87±14.43 11.18 -0.48±11.57 9.26 3.63±16.02*** 12.62

DBP 0.11±8.51 6.49 -1.17±9.08 7.15 1.07±7.92** 5.99

SBP for patient group higher than healthy group (SD ~ 5 mmHg)

DBP estimation of patient group was lower than healthy group (~ 1 mmHg)

***p<0.001, **p<0.01

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35.03 mmHg

-27.70 mmHg

22.20 mmHg

-23.13 mmHg

16.59 mmHg

-14.45mmHg

16.63 mmHg

-18.97 mmHg

Bland-Altman plot of the BP estimations for the healthy group and patient group.


Accuracy: Different Calibration Intervals

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The accuracy significantly decreased 1 week after the calibration

Day 1 Week 1 Week 2 Week 6

Mean±SD

(mmHg)

MAD

(mmHg)

Mean±SD

(mmHg)

MAD

(mmHg)

Mean±SD

(mmHg)

MAD

(mmHg)

Mean±SD

(mmHg)

MAD

(mmHg)

SBP 1.65±10.71 7.81 2.55±14.87 12.18 3.19±14.84 11.99 0.15±16.54 12.76

DBP 1.31±4.94 3.94 -0.16±9.77 7.58 0.53±9.17 7.29 -1.23±9.14 7.17

***p<0.001

The variance of BP estimation error with increase of calibration interval.


•Cardiovascular disorder

- Development of disease would influence the effectiveness of PTT to estimate BP

- Increase the arterial stiffness - alternation of the fluid dynamics - impairment ventricular-arterial coupling (PTT-BP)

•Calibration intervals

- Accuracy decreased with the increase of calibration period

•Explore extra parameters and modify the model to

improve the accuracy for disease population and for

longer calibration interval

Summary

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1

1

1

1


PTT for Respiration Rate Estimation

Capnobase Data

Cardiac Activities Experiment

Results

Closed-loop Model

1

Introduction




Estimation





•Dynamic fluctuations of BP

- High frequency (HF, 0.20-0.35 Hz) – respiratory activity

- Low frequency (LF, 0.01-0.15 Hz) – arterial vasomotion

•PTT: inadequate to follow dynamic BP variations

- PTT mainly presents HF variation

- Evaluation of inspiratory effort, detect breathing events

•Objectives:

- To investigate the relationship of PTT with respiration

- To monitor breathing pattern and measure respiratory rate (RR)


Drinnan, et al. Physiological Measurement (2001). Pitson and Stradling, European Respiratory Journal (1998).

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•Experimental Data

- Recorded signal (fs=300 Hz) (8 min)- Reference respiratory signal - Capnography (EtCO2)

- ECG

- PPG

- 42 subjects (5 excluded)- 19 Spontaneous ventilation (15.8±19.3 years)

- 18 Control ventilation (27.6±20.2)

•Algorithm procedure

- PTT for RR

- Fusion method to improve RR estimation accuracy

- Comparison with Smart Fusion (SF) method

Capnobase Dataset

http://www.capnobase.org/ Karlen et al. TBME (2013).

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Average & SD>4breaths/min (×)The control method

http://www.capnobase.org/


RR Measurement

Respiratory

frequencyRespiratory

frequency

•Number of Breaths (RR#)

- Number of breaths in one minute

• Instantaneous RR (RRinst)

- Peak detection

- RRinst=60

𝑇𝑖𝑚𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑎𝑛𝑑 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑏𝑟𝑒𝑎𝑡ℎ

•RR detected from PSD (RRPSD)

- PSD: pwelch (△f=0.036 Hz)

- RRPSD=1

𝑅𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑜𝑟𝑦 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦

- Physiological range (0.10-0.65 Hz)

22/50


PTT for RR – Capnobase DataPTT

Reference respiratory signal

23/50

PTT varied each breathing cycle, spectrum at respiratory frequency

PTT could predict continuous RR quite well, particularly at stable state

Continuous RR estimations with PTT vs. reference RR


r=0.973.85 brpm

-2.61 brpm

r=0.955.77 brpm

-2.51 brpm

r=0.963.50 brpm

-3.80 brpm

PTT Fusion for RR

PTT, HRI and PRI have

similar variation patterns

with the reference

respiratory signal.

The fused estimations were

highly correlated with

reference RR with good

agreement.

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The fused estimation

significantly agreed better

with the reference than the

controlled method.

6.59 brpm

-5.17 brpm

3.50 brpm

-3.80 brpm

p<0.001Bland-Altman plot of the estimations by the fused RRPSD

with PTT and the control method

Scatter plot of the RRPSD estimations

vs. reference RRPSD


•30 Subjects

- 18 healthy subjects (24.8±2.3 years)

- 12 hypertensive patients (67.8±8.8 years)

•Recorded signal (fs=1000 Hz)

- One-lead ECG

- PPG from left index finger

- Continuous BP (Finapres)

- Respiratory signal (respiratory belt)

•Experimental procedure

- Position transition: supine - standing –sitting

- Maneuvers: DB – VM – HG

Cardiac Autonomic Nervous Activity

25/50


PTT, SBP and Respiration at Cardiac Activities

Supine ASSitting

DB VM HG

PTT varied in phase with the respiratory signal and HF spectral of SBP

26/50

PTT

SBP

Respiration

PTT

SBP

Respiration

PTT

SBP

Respiration

PTT

SBP

Respiration

PTT

SBP

Respiration

PTT

SBP

Respiration

Time series variations of PTT, SBP and respiration and their corresponding spectrum


RR parameters

RR# RRinst RRPSD

RMSE

(breaths/min)1.58 1.95 3.33

MAE

(breaths/min)1.06 1.26 1.64

PTT for RR Cardiac Activities

2.70 brpm

-3.38 brpm

2.91 brpm

-4.27 brpm

6.72 brpm

-6.34 brpm

Different postures

Different maneuvers

27/50

PTT is able to track RR at different positions and maneuvers

Bland-Altman plot of RR from PTT vs. reference RR Estimation error of RR with PTT

Estimation error comparison at different postures

Estimation error comparison at different maneuvers


Effect of Respiration on BPCentral Effect

Mechanical Effect

△V: fluctuation in respiratory volume

△RRI: fluctuation in R-R interval

△SBP: fluctuation in systolic BP

△PP: fluctuation in pulse pressure

△DBP: fluctuation in diastolic BP

△SCO: surrogate cardiac output

RSA: Respiratory Sinus Arrhythmia

ABR: Arterial Baroreflex

DER: Direct Effects of Respiration

CID: Circulatory Dynamics

The effect of respiration on BP:

- Mechanical transmission of intrathoracic pressure to arterial BP

- The respiratory-cardiac coupling through RSA and the circulation dynamics

The HR variability is under regulation:

- Arterial baroreflex mechanism

- RSA

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Simplified Closed-loop Model

𝐏𝐓𝐓~𝒇 ∆𝑽

∆𝐒𝐁𝐏 𝐧 = 𝐢=𝟎

𝐌

𝐇𝐃𝐄𝐑 𝐢, 𝐧 ∙ 𝐏𝐓𝐓 𝐧 − 𝐢 + 𝐢=𝟎

𝐌

𝐇𝐂𝐈𝐃 𝐢, 𝐧 ∙ ∆𝐂𝐎 𝐧 − 𝐢 − 𝛕𝐂𝐈𝐃

∆𝐑𝐑𝐈 𝐧 = 𝐢=𝟎

𝐌

𝐇𝐑𝐒𝐀 𝐢, 𝐧 ∙ 𝐏𝐓𝐓 𝐧 − 𝐢 − 𝛕𝐑𝐒𝐀 + 𝐢=𝟎

𝐌

𝐇𝐀𝐁𝐑 𝐢, 𝐧 ∙ ∆𝐒𝐁𝐏 𝐧 − 𝐢 − 𝛕𝐀𝐁𝐑

29/50

HDER: transfer function

representing the direct influence of

respiratory-related intrathoracic

pressure changes on BP

HCID: transfer function representing

the circulatory dynamics

△CO=△PP/△RRI

HRSA: transfer function representing

respiratory-cardiac coupling

HABR: transfer function

representing baroreflex dynamics

Chaicharn. ProQuest (2007).


•PTT and respiration

- PTT varies with respiration

- PTT could monitor different breathing patterns

•PTT and RR

- PTT is able to predict continuous RR

- Fusion method with PTT for RR estimation derived

improved accuracy

- RR estimation with PTT obtained acceptable accuracy

at different activities

•Hypothesis: PTT is respiratory-modulated

Summary

30/50

1

1

1

1

Background

PPG Intensity Ratio

BP model (1) – M-K Equation

BP model (2) – B-H Equation

Experimental Validation

Results

1

Introduction




Estimation





•Continuous BP

- 1st order – heartbeat- 2nd order – HF variation- 3rd order – LF variation

•PTT

- Respiratory frequency

- 2nd order variation of BP

•3rd order

- Slow variation

- Sympathetic vasomotor

- Arterial diameter change (LF variation)

Background

Pagani, et al. Blood Pressure Variability. Ann N Y Acad Sci. 1996

𝐑𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 =𝟖𝛈𝐋

𝛑𝐑𝟒

32/50

Determinants of arterial BP


Light Source

PP

G W

avef

orm

Io

I

PPG Intensity Ratio (PIR)

Arterial diameter varies with

arterial pressure

I = I0 ∙ e−ε∙c∙l

𝐈𝐋 = I0 ∙ e−αDC∙dDC ∙ e−α∙Ds

𝐈𝐇 = I0 ∙ e−αDC∙dDC ∙ e−α∙Dd

∆𝐝 = Ds − Dd =1

α∙ ln𝐈𝐇𝐈𝐋

PIR =IHIL= eα∙∆d

PPG Intensity Ratio (PIR) can reflect the

relative change of arterial diameter

∆d

Tissues

Venous blood

Non-pulsatile

component of

arterial blood

Pulsatile

component of

arterial blood

Ds

IL

Systolic

Dd

IH

Diastolic

Beer-Lambert Law:

33/50

ε: absorption coefficient

c: concentration of the material

l: effective light path

α= ε•c


•Cardiovascular nervous autonomic function tests- AS – mainly sympathetic

- DB – parasympathetic

- VM – sympathetic

- HG - sympathetic

•ECG, PPG, and continuous BP (fs=1000 Hz)

•5 subjects

•Heart rate interval (RRI), PTT, PIR, SBP, DBP, PP - Time domain

- Spectral domain: - PSD

- LF:HF ratio- evaluation of sympatho-parasympathetic balance

Experimental Analysis of PIR

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PTT mainly coupled with SBP/PP at HF band

PIR coupled SBP/DBP at LF band

SupineRRI

PTT

PIR

SBP

DBP

PP

AS Sitting

DB VM HG

RRI

PTT

PIR

SBP

DBP

PP

RRI

PTT

PIR

SBP

DBP

PP

RRI

PTT

PIR

SBP

DBP

PP

RRI

PTT

PIR

SBP

DBP

PP

RRI

PTT

PIR

SBP

DBP

PP

PIR – LF Variation

PIR evaluate BP regulation in LF range, which is the sympathetic

modulation that is due to the change of arterial diameter

PSD of HRI, PTT, PIR and SBP at different maneuvers

LF:HF ratio

35/50

Time series variations and spectrum at different states


• PP estimation with PTT

(M-K equation)

BP Model (1) –M-K Equation

E =∆P

∆Ro∙2 1 − σ2 RoRi

2

Ro2 − Ri

2 PWV ∝1

PTT

L

PTT=∆P

∆Ro∙2 1 − σ2 RoRi

2

Ro2 − Ri

2 ∙ℎ

𝜌𝐷

PP ∝1

PTT2

𝐏𝐏 = 𝐏𝐏𝟎 ∙𝐏𝐓𝐓𝟎𝐏𝐓𝐓

𝟐

• DBP estimation with PIR

(Windkessel Model)

DBP = P0 ∙ e− t RC

1

PIR= e−α∙∆d

DBP ∝1

PIR

Bergel, et al. The Journal of Physiology (1961). Parker, et al. Medical & Biological Engineering & Computing (2009).

𝐃𝐁𝐏 = 𝐃𝐁𝐏𝟎 ∙𝐏𝐈𝐑𝟎𝐏𝐈𝐑

𝐒𝐁𝐏 = 𝐃𝐁𝐏𝟎 ∙𝐏𝐈𝐑𝟎𝐏𝐈𝐑+ 𝐏𝐏𝟎 ∙

𝐏𝐓𝐓𝟎𝐏𝐓𝐓

𝟐

△P: PP in the artery

Ro: the external radius

△Ro: external radius change

Ri: the internal radius

σ: Poisson’s ratio

P0: end-systolic aortic pressure

R: peripheral resistance

C: the compliance

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𝛒𝐝


•Subjects

- 27 healthy adults (14 males, 25.6±2.1 years)

- Rest at seated position

•Recorded signals (fs=1000 Hz)

- Finapres BP (left arm and left thumb)

- One-lead ECG (left and right arms)

- PPG (left index finger)

•Accuracy evaluation

- Mean±SD, MAD

- Bland-Altman plot

- Comparison with other two control methods


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𝐏𝐓𝐓𝐌𝐞𝐭𝐡𝐨𝐝 𝟐 :

𝐃𝐁𝐏 = 𝐌𝐁𝐏𝟎 +𝟐

𝛄𝐥𝐧𝐏𝐓𝐓𝟎

𝐏𝐓𝐓−𝟏

𝟑𝐏𝐏𝟎 ∙

𝐏𝐓𝐓𝟎

𝐏𝐓𝐓

𝟐

𝐒𝐁𝐏 = 𝐃𝐁𝐏 + 𝐏𝐏𝟎 ∙𝐏𝐓𝐓𝟎

𝐏𝐓𝐓

𝟐

𝐏𝐓𝐓𝐌𝐞𝐭𝐡𝐨𝐝 𝟏 :

𝐒𝐁𝐏 = 𝐒𝐁𝐏𝟎 −𝟐

𝛄𝐏𝐓𝐓𝟎𝐏𝐓𝐓 − 𝐏𝐓𝐓𝟎

Chen, et al. Medical & Biological Engineering & Computing (2000). Poon and Zhang. EMBC’05 (2005).


Overall BP estimations against the reference BP.

Overall Accuracy

SBP

MBP

DBP

SBP

MBP

DBP

SBP DBP MBPr=0.91 r=0.88 r=0.89

Mean±SD: -0.37±5.21

MAD: 4.09

Mean±SD: -0.08±4.06

MAD: 3.18

Mean±SD: -0.18±4.13

MAD: 3.18

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Scatter diagram of estimated BP vs. reference

Bland-Altman plot of estimations with the reference


Beat-to-beat BP estimation by the proposed methods (green), the control

methods (blue) with the reference (red) as the benchmark.

Comparison with Other Methods

PTT Method (1) PTT Method (2) Proposed Method

Mean

(mmHg)

SBP 0.19 -0.11 -0.37

DBP - 0.19 -0.08

MBP - 0.09 -0.18

SD

(mmHg)

SBP 6.21 7.31 5.21*†

DBP - 6.03 4.06†

MBP - 6.25 4.13†

MAD

(mmHg)

SBP 4.94 5.76 4.09*†

DBP - 4.80 3.18†

MBP - 4.96 3.18†

* compare with PTT method (1) with p<0.05;

† compare with PTT method (2) with p<0.05.

The proposed BP model achieved better accuracy than the two

compared PTT methods in estimating SBP/DBP/MBP

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BP Model (2) – B-H Equation

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• MBP is determined by

- Cardiac output, systemic vascular

resistance

- LF variation

• PP derivation – B-H equation

∆d = Ds − Dd =1

α∙ lnPIR ≈

PIR

α

𝐌𝐁𝐏 = 𝐌𝐁𝐏𝟎 ∙𝐏𝐈𝐑𝟎𝐏𝐈𝐑

𝐏𝐖𝐕 =𝐕

𝛒∆ 𝐕 ∆𝐏

∆V

V ∙ ∆P=Ds2 − Dd

2

Dd2 ∙ PP

=Ds + Dd

Dd2 ∙∆d

PP

L

PTT=

1

ρ ∙ (Ds + Dd ) Dd2 ∙PP

∆d

PP =ρL2 Ds + Dd

Dd2 ∙ ∆d ∙

1

PTT2PP ∝

PIR

PTT2

𝐏𝐏 = 𝐏𝐏𝟎 ∙𝐏𝐈𝐑

𝐏𝐈𝐑𝟎∙𝐏𝐓𝐓𝟎

𝐏𝐓𝐓

𝟐

𝐒𝐁𝐏 = 𝐌𝐁𝐏𝟎 ∙𝐏𝐈𝐑𝟎𝐏𝐈𝐑+𝟐

𝟑∙ 𝐏𝐏𝟎 ∙

𝐏𝐈𝐑

𝐏𝐈𝐑𝟎∙𝐏𝐓𝐓𝟎𝐏𝐓𝐓

𝟐

𝐃𝐁𝐏 = 𝐌𝐁𝐏𝟎 ∙𝐏𝐈𝐑𝟎𝐏𝐈𝐑−𝟏

𝟑∙ 𝐏𝐏𝟎 ∙

𝐏𝐈𝐑

𝐏𝐈𝐑𝟎∙𝐏𝐓𝐓𝟎𝐏𝐓𝐓

𝟐

𝐌𝐁𝐏 =𝟏

𝟑𝐒𝐁𝐏 +

𝟐

𝟑𝐃𝐁𝐏

𝐏𝐏 = 𝐒𝐁𝐏 − 𝐃𝐁𝐏

• PIR

- Arterial diameter change –

peripheral resistance

- LF variations of BP


•Subjects

- 10 healthy adults (6 males, 24.9±1.9 years)

- Rest at seated position

•Recorded Signals (fs=1000 Hz)

- One-lead ECG (left and right arms)

- PPG (left index finger)

- Finapres BP (left arm and left thumb)

•Accuracy evaluation

- Finapres BP – reference

- Mean±SD

- Bland-Altman plot


41/50

MethodMean

(mmHg)

SD

(mmHg)

MAD

(mmHg)

Proposed

method

SBP -0.41 5.15* 4.18*

DBP -0.84 4.05† 3.43†

PTT Method

(1)

SBP 0.62 7.19 5.82

DBP 1.18 6.12 5.03

*SBP: Significant different level <0.05; †DBP: significant different level <0.05;

The proposed BP model agreed good with the reference

Achieved better accuracy than one of the control method for SBP/DBP

9.68 mmHg

-10.05 mmHg

7.10 mmHg

-8.78 mmHg

Bland-Altman plot of SBP and DBP estimation.


PTT-BP Model

SBP DBP MBP

PM1 SBP = DBP + PP0 ∙PTT0PTT

2

DBP = DBP0 ∙PIR0PIR

MBP =1

3SBP +

2

3DBP

PM2 SBP = MBP0 ∙PIR0PIR+2

3∙ PP0 ∙

PIR

PIR0∙PTT0PTT

2

DBP = MBP0 ∙PIR0PIR−1

3∙ PP0 ∙

PIR

PIR0∙PTT0PTT

2

MBP = MBP0 ∙PIR0PIR

CM1 SBP = SBP0 −2

γPTT0∙ PTT − PTT0 DBP = DBP0 −

2

γPTT0∙ PTT − PTT0 MBP =

1

3SBP +

2

3DBP

CM2 SBP = DBP + PP0 ∙PTT0PTT

2

DBP = MBP0 +2

γlnPTT0PTT−1

3PP0 ∙

PTT0PTT

2

MBP = MBP0 +2

γlnPTT0PTT

CM3 SBP = a3 ∙ PTT + b3 DBP = a3′ ∙ PTT + b3

′MBP =

1

3SBP +

2

3DBP

CM4 SBP = a4 ∙ lnPTT + b4 DBP = a4′ ∙ lnPTT + b4

′MBP =

1

3SBP +

2

3DBP

CM5 SBP =a5PTT+ b5 DBP =

a5′

PTT+ b5

′ MBP =1

3SBP +

2

3DBP

CM6 SBP =a6PTT2+ b6 DBP =

a6′

PTT2+ b6

′ MBP =1

3SBP +

2

3DBP

Comparison Study

Chen, et al. Medical & Biological Engineering & Computing, 2000.

Poon, et al. EMBC’05, 2005.

Douniama, et al. Computer in Cardiology, 2009.

Muehlsteff, et al. EMBC’06, 2006.

Mase, et al. Journal of Electrocardiology. 2011.

Proposed method (PM); Comparison method (CM)

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Subjects

(n=33)

Normotensive Group

(n=19)

Hypertensive Group

(n=14)

Mean age (range) 43 (21-77) 26 (21-47) 67 (43-77)

Hypertension (n) 14 0 14

Prehypertension 7 0 7

Stage I hypertension 6 0 6

Stage II hypertension 1 0 1

SBP (mmHg) 121.12±19.52 107.74±10.04 139.30±13.50

DBP (mmHg) 68.92±8.16 65.21±6.96 74.00±7.00


•Subjects (N=33)

•Recorded signal- ECG, PPG, Finapres BP

•Experiment protocol- Different positions (supine, standing, sitting); DB, VM, HG

- Following on the second day (8 subjects, sitting rest)

43/50


Overall Accuracy Comparison

SBP DBP MBP

PM better than CM; PM1-PM2, CM1-CM2, SD<8 mmHg

44/50

Indicating the best performance

Overall estimation error of the proposed methods (PM) and comparison methods (CM) at

steady state.


Comparison of Different Maneuvers

Supine

45/50

HGVMDB

Sitting AS

PM1 and PM2 on the whole were better than other methods, followed by CM1-CM2

Physiological model provides more accurate estimation that regression model


Estimation errors at different positions and different maneuvers.


Different Calibration Interval

***

***

***

***

***

***

***

***

***

46/50

Estimation error significantly increased second day after the initial calibration

Accuracy by using proposed method remained better for SBP and MBP estimation


Estimation error of the Day 1 and Day 2 after the initial calibration for PM1-PM2, CM1-CM2.


•A new parameter PIR

- Relative change in arterial diameter

- Evaluate LF variation of BP

•Two BP models established with PTT and PIR

- PTT could reflect HF range of BP

- PTT for LF variation of BP, and arterial diameter change

•The performance of the proposed models

- Comparison of other PTT methods

- Extended calibration interval

Summary

47/50

•The combination of PTT and PIR improved the accuracy of BP estimation

- The 2nd and 3rd order variation

- Short-term variability

•The accuracy over

the second day decreased

- The circadian fluctuations?

- Long-term variability (24 h)

•Controlling mechanism of

circadian BP rhythm

- Hemodynamic circadian rhythms

- Neural and hormonal (Sympatho-adrenergic mechanism)Furlan, et al. Circulation (1990). Millar-Craig, et al. Lancet (1978).

1

1

1

1

Conclusions

Suggestions for Future Work

1

Introduction




Estimation





•The examination of the impact of the cardiovascular disease and calibration interval on the accuracy of PTT-based cuffless BP estimation

•A new interpretation of PTT for its contribution to track BP changes; the usage of PTT for continuous RR estimation

•Propose of a new indicator, PIR, to evaluate the LF variation of BP

•Establishment of novel BP models with the integration of PTT and PIR to improve the estimation accuracy

Contribution

49/50


•To further validate the proposed models

- Intra-arterial BP as reference

- Following standard IEEE 1708

•To improve the accuracy for subject with CVDs

- Potent parameter other than PTT

- Modify models

•To address the calibration issue

- Investigate the minimal interval ensuring the accuracy

- Explore ways to extend the calibration interval

Future Work

50/50

Prof. Hon Ki Tsang

Prof. Yuanting Zhang

Prof. Ni Zhao

Prof. Carmen Poon

Prof. Toshiyo Tamura

Prof. Bryan Yan

Prof. Walter Karlen

Acknowledgement

Jing Liu

Wenxuan Dai

Yali Zheng

Peng Su

EE Colleagues- Terahertz group- Robotics group- Biosensing group

Friends

Wei and my family

Journal publications:

Ding, X., Zhang, Y., Liu, J., Dai, W., & Tsang, H. (2016). Continuous Cuffless Blood Pressure Estimation Using Pulse TransitTime and Photoplethysmogram Intensity Ratio. IEEE Transactions on Biomedical Engineering, 63(5), 964-972.

Ding, X., Zhang, Y., & Tsang, H. (2016). Impact of Heart Disease and Calibration Interval on Accuracy of Pulse Transit TimeBased Blood Pressure Estimation. Physiological Measurement, 37(2), 227-237.

Zheng, Y., Ding, X., Poon, C., Lo, B., Zhang, H., Zhou, X., Yang, G., Zhao, N. & Zhang, Y. (2014). Unobtrusive Sensing andWearable Devices for Health Informatics. IEEE Transactions on Biomedical Engineering, 61(5), 1538-1554.

Ding, X., Yan, B., Karlen, W., Zhang, Y., Liu, J., Dai, W., & Tsang, H. Estimation of Respiratory Rate with Pulse Transit timeunder the modulation of Cardiac Autonomic Nervous Activity. (Preparing)

Ding, X., Zhang, Y., Yan, B., Liu, J., Dai, W., & Tsang, H. A Comprehensive Study on Pulse Transit Time Based ContinuousCuffless Blood Pressure Measurement. (Preparing)

Newsletter:

Ding, X., Liu, J., Zhao, N., & Zhang, Y. T. (2014). Cardiovascular Health Informatics: Wearable Medical Device and FlexibleBiosensor for m-Health. IEEE Life Science Newsletter, Mar 2014.

Conference publications:

Ding, X., Zhang, Y., Tsang, H., & Karlen, W. (2016). A Pulse Transit Time Based Fusion Method for the Noninvasive andContinuous Monitoring of Respiratory Rate. 38th Annual International Conference of the IEEE Engineering in Medicine andBiology Society, (EMBC’16) (Accepted).

Ding, X., Zhang, Y., & Tsang, H. (2016). A New Modeling Methodology for Continuous Cuffless Blood PressureMeasurement. Proceedings of the International Conference on Biomedical and Health Informatics, (BHI 2016), Las Vegas,2016.

Ding, X., Jing, L., Dai, W., Carvalho, P., Magjarevic, R., & Zhang, Y. (2015). An Attempt to Define the Pulse Transit Time.Proceedings of the International Conference on Biomedical and Health Informatics (ICBHI 2015), Haikou, China.

Ding, X., Dai, W., Luo, N., Liu, J., Zhao, N., & Zhang, Y. (2015). A Flexible Tonoarteriography-Based Body Sensor Networkfor the Cuffless Measurement of Arterial Blood Pressure. Proceedings of 12th Annual Body Sensor Networks Conference2015 (BSN 2015), MIT, Cambridge, USA.

Publication

Conference publications:

Ding, X., & Zhang, Y. (2015). Photoplethysmogram Intensity Ratio: A Potential Indicator for Improving the Accuracy ofPTT-Based Cuffless Blood Pressure Estimation. Proceedings of the 37th Annual International Conference of the IEEEEngineering in Medicine and Biology Society. Milan, Italy, 2015.

Liu, J., Zhang, Y., Ding, X., Dai, W., & Zhao, N. (2016). A Preliminary Study on Multi-Wavelength PPG Based Pulse TransitTime Detection for Cuffless Blood Pressure Measurement. 38th Annual International Conference of the IEEE Engineering inMedicine and Biology Society, (EMBC’16) (Accepted).

Liu, J., Li, Y., Ding, X., Dai, W., & Zhang, Y. T. (2015). Effects of Cuff Inflation and Deflation on Pulse Transit TimeMeasured from ECG and Multi-Wavelength PPG, Proceedings of the 37th Annual International Conference of the IEEEEngineering in Medicine and Biology Society, Milan, Italy, 2015.

Ding, X., Zheng, Y., Dai, W., & Zhang, Y. T. (2014). Changes in Blood Pressure with Different Postures While Swallowing.The International Conference on Health Informatics, pp. 179-181.

Dai, W., Ding, X., Zheng, Y., & Zhang, Y. T. (2014). Amplitude Index for the Quality Assessment of Pulsatile Signals inNoise. The International Conference on Health Informatics, pp. 228-230.

Ding, X., Zhang, Y. T. (2014). Cuff-less Continuous Blood Pressure Estimation with Pulse Transit Time andPhotoplethysmogram Intensity Ratio. Proceedings of International Biomedical Engineering Conference 2014, pp. 351.

Book Chapter:

Poon, C., Zheng, Y., Luo, N., Ding, X., & Zhang, Y. T. (2014). Wearing Sensors Inside and Outside of the Human Body forthe Early Detection of Diseases. Wearable Sensors. Elsevier Inc.

Patents:

Zhang Y., Ding, X., Liang, Y., Liu J., Dai, W., and Yuan, S (2015). License Agreement on software “m-Health and WearableTechnologies for Physiological Watch” with Huawei Device (Dongguan) Co., Ltd.

Zhang, Y., Tsang, H, Zhao, N, Ding, X., Liu, J, and Dai, W. Unobtrusive Multi-sensor Array for Pulse Wave velocity/BloodPressure Imaging. (Pending).

Zhao, N., Zhang, Y., Liu, J, Ding, X., Dai, W., Yuan, S., Li Yao. Method for Measuring Cardiovascular and RespiratoryParameters Based on Multi-Wavelength Photoplethysmography. (Provisional)

Publication

Thank you!

Documents

Continuous Cuffless Blood Pressure Estimation with ...users.ox.ac.uk/~engs1850/PhD_oral_defense_XiaorongDing.pdf · (n=48) Mean age (range) 57 (18-96) 27 (22-54) 80 (78-96) Gender