Development of Blood Pressure Estimation Algorithm Using Variable Characteristic Ratios on Oscillometric Method

Ⅰ. 서 론

(blood pressure)

. (invasive)

(non-invasive)

[1].

(auscultatory method), (oscillometric method), (electronic palpation method) . ,

[2]

[3-4]

.

(systolic blood pressure, SBP),

(diastolic blood pressure, DBP)

(mean arterial pressure, MAP) (cuff) SBP DBP

[5]. SBP DBP

(characteristic ratio) (height-based) (slope-based) .

[6].

(artefact)

. Moraes[7]

신 준

Development of Blood Pressure Estimation Algorithm Using Variable Characteristic Ratios on Oscillometric Method

Joon Shin

Department of Mechanical and Automotive Engineering, Gangneung-Wonju National University (Received August 3, 2009. Accepted November 11, 2009)

In this paper, variable characteristic ratio algorithm based on oscillometric method is proposed to enhance the accuracy of blood pressure measurement. We combined the slope-based approach and fuzzy inference technique to change the characteristic ratios of height-based method. The proposed algorithm was assessed on 255 measurements from 85 subjects and compared with the conventional height-based algorithm. The testing results showed that the developed algorithm achieved an overall grade A for both systolic and diastolic blood pressures according to the BHS protocol. And, mean standard deviation between the observers and the developed algorithm were 5.71mmHg and 6.29mmHg for systolic and diastolic pressures respectively, which also fulfilled the AAMI criteria. In conclusion, this algorithm was successfully developed and recommended for further clinical trials with the wider adult population.

Blood pressure(혈압), Oscillometric method(진동법), Fuzzy inference (퍼지 추론), Variable characteristic ratio(가변 특성비)

Corresponding Author :

(220-711)

Tel : +82-33-760-8744 / Fax : +82-33-760-8741

E-mail : [email protected]

본 연구는

2008

, Colak[8] - . Al-Jaafreh[9] PPG 2

, Huo[10]

, Lin[11]

. ,

, .

.

Ⅱ. 혈압추정 알고리즘 개발

A. 특징 특징 추출 특징 특징 추출 추출 추출

.

.

Fig. 1 (increasing

section) (decreasing section)

(SBP bas )

(DBP bas ) .

. Fig. 1

0.5~30Hz DC

2Hz (trough)

.

(heart rate) .

0.5~1.5

.

, (rule)

[12,13].

(SBP slp )

SBP bas

.

(DBP slp ) DBP bas

. Fig. 1

(MCP max ) MAP

,

3

A m p litu d e (V )

2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5

Increasing section Decreasing section

Time (sec) 0

-2

5 10 15 20

MCP

그림 1. 가압대 압력센서의 진동신호와 특징들의 정의 .

Fig. 1. Oscillation signal of cuff pressure sensor and definition of several features.

(MCP inc ) MCP max

.

(MBP dec ) MAP

. MCP inc

(p-p) ,

MBP dec

. ,

MCP dec /MCP max

MBP dec /MAP

MCP dec /MCP max (correlation value) 0.39

MBP dec /MAP 0.58 .

B. 퍼지 퍼지 퍼지 퍼지 추론 추론 추론 추론

.

[14].

0 1

(membership function) .

Fig. 2

(a) (b) 2

. Fig. 2 (a) x

3 “Negative

(NE)”, “Approximately Zero(AZ)”, “Positive(PO)”

, Fig. 2 (b) x

“Low(LO)”, “Medium(ME)”, “High

(HI)” .

, Fig. 3

”Negative Big(NB)”, “Negative Small(NS)”, “Approximately Zero(AZ)”, “Positive Small(PS)”, “Positive Big (NB)” 5

.

9

3 27

. SBP bas -SBP slp

±5mmHg ,

MCP inc /MCP max 27

0.669, 0.054 Fig. 2 (b)

.

0.687, 0.062

Fig. 3 .

2 1

M e m b e rs h ip v a lu e 1.0

0.0 NB

0.75 Systolic characteristic ratio

0.72 0.69 0.66 0.63

NS AZ PS PB

그림 3. 수축기 퍼지 출력변수에 대한 관계 함수 . Fig. 3. Membership function of fuzzy sets for systolic output.

1.0

M e m b e rs h ip v a lu e

0.0

NE AZ PO

10 0

-10

SBP

-SBP

(mmHg)

M e m b e rs h ip v a lu e 1.0

0.0

LO ME HI

0.77 0.67

0.57

MCP

/MCP

(a) First input to systolic fuzzy inference engine (b) Second input to systolic fuzzy inference engine 그림 2. 수축기 퍼지 입력변수에 대한 관계 함수 .

Fig. 2. Membership functions of fuzzy sets for systolic input.

DBP bas -DBP slp MBP dec /MAP .

0.748, 0.034

0.690, 0.102

.

, 9 Table 1

Table 2 .

Mandani -

[15]. ,

.

Ⅲ. 실험 및 결과 고찰

A. 실험장치 실험장치 및 실험장치 실험장치 및 및 방법 및 방법 방법 방법

85 3

255

.

.

5 3

2 2 (3M Littmann,

USA)

±5mmHg

.

, DAQCard-AI-16XE

-50(National Instrument, TX, USA) 250

LabView .

NIBP (Welch Allyn, USA) 3~4mmHg .

, Fig. 4

. B. 실험결과 실험결과 실험결과 실험결과 고찰 고찰 고찰 고찰

2

. /

(American Association for the Advancement of Medical Instrumentation, AAMI) , A~

D (British Hypertension

Society, BHS) . AAMI

±5mmHg

±8mmHg . BHS 60%

input 1

input 2 NE AZ PO

LO NB NS AZ

ME NS AZ PS

HI AZ PS PB

a. Input 1은 SBP

-SBP

이다.

b. Input 2는 MCP

/MCP

이다.

표 1. 수축기 특성비 추출을 위한 9 개의 규칙과 판단 테이블 .

Table 1. Decision table for systolic characteristic ratio and 9 fuzzy rules.

input 1

input 2 NE AZ PO

LO AZ NS NB

ME PS AZ NS

HI PB PS AZ

a. Input 1은 DBP

-DBP

이다.

b. Input 2는 MBP

/MAP이다.

표 2. 이완기 특성비 추출을 위한 9 개의 규칙과 판단 테이블 .

Table 2. Decision table for diastolic characteristic ratio and 9 fuzzy rules.

압력구간(상승, 하강) 설정 혈압 신호 입력

특징 추출 (MCP

, MCP

, MBR

, MAP)

경사혈압(SBP

, DBP

) 계산 기본혈압(SBP

, DBP

) 계산

퍼지 추론

특성 비 추출

최종 혈압 계산

그림 4. 개발된 알고리즘에 대한 흐름도 .

Fig. 4. Flowchart of the developed algorithm.

±5mmHg 85% ±10mmHg

95% ±15mmHg A

. Table 3 BHS .

Fig. 5

Bland-Altman .

Fig. 5 (a) (b)

. , AAMI

0.15mmHg, 5.71mmHg

-0.21mmHg, 6.29mmHg . BHS

Table 4

Conventional

Curve-fitting

. ,

AAMI . ,

100mmHg 180mmHg

10% 60mmHg

100mmHg 10%

. 14.7% 100mmHg

17.8% 60mmHg

.

25cm 35cm

10% 25cm

12.8% 35cm .

AAMI

BHS A

.

.

Grade

Absolute difference between reference and test device (%)

< 5mmHg < 10mmHg < 15mmHg

A 60 85 95

B 50 75 90

C 40 65 85

D Worse than C

표 3. BHS 기준의 등급판정 기준 . Table 3. Grade criteria used by BHS.

20

15

10

5

0

-15

-20

60 70 80 90 100

Average BP of auscultatory & developed algorithm (mmHg) 110 120 130 140 150 160 Mean - 1.96SD

Mean + 1.96SD

Mean SB P D iff e re n c e (m m H g ) -5

-10

20

15

10

5

0

-15

-20

30 40 50 60 70

Average BP of auscultatory & developed algorithm (mmHg)

80 90 100 110

Mean - 1.96SD Mean + 1.96SD

Mean

D B P D iff e re n c e (m m H g )

-10 -5

(a) Systolic blood pressure (b) Diastolic blood pressure 그림 5. 개발된 알고리즘과 청음법 결과에 대한 Bland-Altman 도표 .

Fig. 5. Bland-Altman plot of the developed algorithm and auscultatory result.

Ⅳ. 결 론

. ,

1

. ,

. ,

BHS A

AAMI .

3

.

참고문헌

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[4] C.C. Tyan, S.H. Liu, J.Y. Chen, J.J. Chen, W.M. Liang, “A Novel

Noninvasive Measurement Technique for Analyzing the Pressure Pulse Waveform of the Radial Artery”, IEEE Trans.

Biomed. Eng., Vol.55, No.1, pp.288-297, 2008.

[5] L.A. Geddes, M. Vaelr, C. Combs, D. Rcincr, C.F. Babs,

“Characterization of the Oscillometric Method for Measuring Indirect Blood Pressure”, Ann. Biomed. Eng., vol.10, pp.271-280, 1982.

[6] G. Drzewiecki, R. Hood, H. Apple, “Theory of the Oscillometric Maximum and the Systolic and Diastolic Detection Ratios,” Ann.

Biomed. Eng., vol. 22, pp.88-96, 1994.

[7] JCTB Moraes, M. Cerulli, PS. Ng, “Development of a New Oscillometric Blood Pressure Measurement System”, IEEE Computers in Cardiology, Vol.26, pp.467-470, 1999.

[8] S. Colak, C. Isik, “Systolic Blood Pressure Classification”, Proc.

Int. Joint Conf. Neural Networks, Vol.1, pp.627-630, 2003.

[9] M.O. Al-Jaafreh, A.A. Al-Jumaily, “Type-2 Fuzzy System Based Blood Pressure Parameters Estimation”, 2nd Asia Int. Conf.

Modelling & Simulation, pp.953-958, 2008.

[10] C. Huo, J. Wang, J. Zhang. S. Liu, “Blood Pressure Measurement Based on Wavelet Analysis and Fuzzy Inference Method”, Proc.

6th World Cong. Intelligent Control and Automation, pp.4382- 4386, 2006.

[11] C.T. Lin, S.H. Liu, J.J. Wang, Z.C. Wen, ”Reduction of Interference in Oscillometric Arterial Blood Pressure Measurement Using Fuzzy Logic,”, IEEE Trans. Biomed. Eng., Vol 50, No.4, pp.432 -441, 2003.

[12] JCTB Moraes, M. Cerulli, PS. Ng, “A Strategy for Determination of Systolic, Mean and Diastolic Blood Pressures from Oscillometric Pulse Profiles”, IEEE Computers in Cardiology, Vol.27, pp.211-214, 2000.

[13] M. Ursino, C. Cristalli, “A Mathematical Study of Some Biomechanical Factors Affecting the Oscillometric Blood Pressure Measurement”, IEEE Trans. Biomed. Eng., Vol. 43, No.8, pp.761-778, 1996.

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[15] C.T. Lin, C.S.G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Englewood Cliffs, Prentice- Hall, 1996.

BP estimation algorithm

Absolute difference (%) BHS

Grade

< 5mmHg < 10mmHg < 15mmHg

Systolic

Conventional 54.51 78.04 89.41 B

Curve-fitting 60.78 88.63 95.69 A

Proposed 64.31 93.33 99.21 A

Diastolic

Conventional 47.46 76.86 90.59 C

Curve-fitting 54.12 79.61 95.69 B

Proposed 62.75 87.84 98.04 A

표 4. 각 알고리즘에 의한 결과와 청음법 결과 사이의 차이

Table 4. Differences between the readings performed by the observers and results obtained by each algorithms