DEVELOPMENT OF HEART RATE MONITOR

USING PHOTOPLETHYSMOGRAPH Anju Annie Jacob, Mr.R.Jegan

Dept of Electronics and instrumentation engineering Karunya University, Coimbatore, India

*Corresponding author:annjacob93@gmail.com

Abstract— this paper presents the photoplethysmography

technology to measure the heart rate of a human being. The

continuous measurements of the physiological parameters are

important to the aged people and critical patients. This is

commonly monitored by pulse oximeter. Today heart related

diseases are rapidly increasing among the population, because

people are undergoing with high pressure from their study’s and

works, so they don’t have enough time to take care of health. In

this paper, we compare the real time measured heart rate of a

person in both MATLAB and Lab VIEW software. Here PPG

signal were taken by PPG sensor but the PPG signal is mostly

distorted by patient’s hand movement. In this paper Kalman

filter is used for removing the motion artifacts because it gives

reliable information from the reconstructed PPG signal and the

pulse rate can be determined by the peak detection algorithm in

Lab VIEW signal processing module.

Keywords— Photoplethysmography, Heart Rate, Kalman Filter,

Motion Artifact.

I. INTRODUCTION

Cardiopathy has become a very serious disease in modern

community, because of many people are undergoing with high

pressure from their study and work. They don’t have enough

time to take care of their health, so here we use a suitable and

non-invasive method to improve the measurement of heart fatal

sign at home. Photoplethysmography was firstly proposed by

Hertzman for measuring the fatal sign, such as heart rate and

SpO2.

Heart rate is used to measure the number of beats

per minute, which is the most important parameter and it is

related to the safety of the Humans. Heart rate reflects the

pulse of Human ventricular and atrium cycle of contraction and

diastole. Blood with oxygen began to spread along the whole

arterial system. The information of the shape, intensity, speed

and rhythm of the pulse wave is a large part of the

physiological and pathological characteristics of human

cardiovascular system.

Based on the literature view, an optical pulse oximeter

sensor was designed and developed by the required software

algorithms. The PPG signals were extracted and which used to

calculate the heart rate and saturation of oxygen. But, the

measured vital signs are independent of most external

environment [1].

A power optimized photoplethysmographic sensor interface

to sense arterial oxygen saturation. But in the designing for the

worst case the result of excessive power consumption is

occurred in most situations [2]. Z. Zhang et al proposed a novel

method; it consists of three methods, signal decomposition,

which is used to partially remove the motion artifacts from the

PPG signal. Second it describe a sparse signal recovery based

spectrum estimation, and third spectral peak tracking. The

sparse signal recovery-based spectrum estimation is used to

eliminate the drawbacks of conventional power spectrum in the

PPG spectrum estimation and which is help to find the spectral

peaks corresponding to heartbeat in the third method. Later,

this method was enhancing by using an advanced sparse signal

recovery model and SS [4]. But the main drawback of this

method is the spectrum calculation of heavy computational

load. A simple and efficient approach based on adaptive step-

size least mean squares adaptive filter for reducing motion

artifacts in corrupted PPG signals [5]. The adaptive filter

techniques are used for removing the motion artifacts from the

PPG signals [6]–[8]. The novelty of the proposed technique is

the synthetic noise reference signal for an adaptive filtering

process, representing motion artifacts noise, which is occurred

from the corrupted PPG signal itself instead of using any

additional hardware such as accelerometer. In [7], the synthetic

noise reference was generated using fast Fourier transform

(FFT) technique. In this paper, M. R. Ram et al present two

more methods; one is using SVD and another using ICA for

the generation of MA noise reference signal. The evaluation of

different wavelets techniques for reduction of motion artifacts

from PPG signals. Wavelet analysis has been carried out on the

PPGs corrupted by the movement of fingers such as bending

finger, vertical and horizontal motions of finger. The results

revealed two important facts. Firstly, the Sp02 values measured

from motion artifacts then reduced PPG signals by different

wavelets and finally the Daubechies wavelet is used to

resorting respiratory information while removing motion

artifacts. Hence, Daubechies wavelet is the mostly preferred to

pulse oximetry applications [9]. Adaptive filtering is a popular

approach to remove motion artifacts, which provided that a

reference signal. The reference signal can be acquired by extra

hardware such as accelerometer [10].

This paper is organized as follows: In section2,

describes the photoplethysmography. In Section 3, it explains

the materials and methods used in this project. In section 3, the

results and discussion have been explained and finally

concluded the project in Section 4.

II. MATERIALS AND METHODS

A. Pulse Oximeter

Pulse oximetry is a way to measure how much oxygen is

carrying in our blood. By using a small device called a pulse

oximeter, the blood oxygen level of our blood can be checked

without needing to be stuck with a needle. The pulse oximetry

can be measured both oxygenated and non oxygenated

hemoglobin which have different wavelength, one is with 660

nm (red light) and the wavelength of other is 940 nm (infarred

light). The oxygenated hemoglobin (Hbo) absorbs IR light and

reflect red light while non-oxygenated hemoglobin (Hb) absorb

more red light and reflect IR light.

B. System Block Diagram

Figure1. Block diagram of the proposed heart rate measurement

The PPG signal were measured with PPG sensor and

three-axis accelerometer (ADXL 335) was attached to the

finger to detect the patient’s hand movement. The PPG signals

is distorted by the motion artifacts were collected on the right

index finger during finger or arm movement. Out of the three-

dimensional data of the accelerometer, two dimensional signal

was taken and analyse which was maximally correlated with

the PPG signal for the removal of motion artifacts.and the

pulse rate of a subject is determined by using peak detection

algorithm in Lab VIEW signal processing module.

C. PPG Acquisition

Photoplethysmography is a non-invasive method to

measure the heart rate that uses the ability of light to reflect

and penetrate in human tissue. With every pulse the blood

vessels increase in the thickness and PPG detects the

volumetric changes in arterial vessels that cause a change in

the light absorption, reflection and therefore the light intensity

detected by the photo detector. The measurement system

consists of a LED, a photo detector and an electric system to

filter and amplify the signal.

The PPG follows cardiac rhythms the signal was used

to determine the patient’s heart rate. This was done by

measuring the period between maximum peaks of the

measured PPG signal and multiplying by 60 (1 minute) to

obtain the measurement per minute; [2] the equation for

calculating the heart rate is,

BPM=Frequency x 60.

C. ADXL 335 Accelerometer

The three axis accelerometer is normally used to find

the movements across the three axis, i.e. x-axis, y-axis, z-axis.

The accelerometer is an electronic device which is interfaced

by using I2C protocol. Here we have used ADXL335

accelerometer. The ADXL335 accelerometer is a small, thin,

low power, complete 3-axis accelerometer with signal

conditioned voltage outputs IC. The VCC takes up to 5V in

and regulates it to 3.3V with an output pin. The ADXL335

accelerometer which measures the dynamic acceleration

(motion, shock, or vibration) and static acceleration (tilt or

gravity) over a ±3 g range with 0.3% nonlinearity and

0.01%/°C temperature stability.

E. Use of Kalman Filter and Measuring Heart Rate

The motion artifact occurs due to the displacement of the

sensor probe by the hand movement of patient during the

treatments such as waving, shaking and rubbing etc. It is

difficult to remove the motion artifacts after the frequency

band overlaps with the PPG signal. The kalman filter shows a

good result in to remove the motion artifacts from the signal.

The Kalman filter estimates a process by using in the

form of feedback control: the filter estimates the process state

and then obtains the feedback in the form of (noisy)

measurements.The kalman filter has two steps: the prediction

step, where the next state of the system is predicted given the

previous measurements, and the update step, where the current

state of the system is estimated given the measurement at that

time step.

In cardiovascular system, heart rate measuring is one

of the important characteristics of the human being. The heart

rate of a healthy adult is around 72 beats per minute (bpm). In

the case of babies have a much higher heart rate at around 120

bpm, while older children have heart rates at around 90 bpm.

The heart rate increases gradually during exercises and returns

slowly to the normal value after exercise. The heart rate is

lower than the normal heart rate which is known as

bradycardia, while higher than normal heart rates are known as

tachycardia

III. RESULTS AND DISCUSSION

Photoplethysmography signal has been acquired from

the subject using a PPG sensor.The signals were obtained for

about a mintue after the subject completely relaxes.The

accelerometer senses the motion of the finger and mostly the

PPG Sensor

and

Accelerometer

Kaman Filter

Implementation My DAQ

Device

Peak Detection

Methods

Heart Rate

Measurement

obtained PPG signal is corrupted by the movement of patient’s

hand. Inorder to eliminate the motion artifacts from the PPG

signal by using kalman filter. In the figure 7 shows the block

diagram of the Lab VIEW for measuring the heart rate of the

patient in bpm and the heart rate can be determined by the peak

detection algorithm in Lab VIEW and MATLAB. In the figure

2 shows the original PPG signal which acquire from the PPG

sensor. In figure 3 describes the noisy signal since the noise is

eliminated by using kalman filter in the figure 4. Then figure 5

shows the superposition graph of original raw PPG signal,

noise signal and filtered signal.

Figure 2.Orignial PPG signal

Figure 3.Noisy signal.

Figure 4.Filtered signal

Figure 5. Superposition Representation

Figure 6-8 shows the result for measuring the heart rate of a

person in both MATLAB and LAB view software. The heart

rate of a normal healthy person is in between 60-90 bpm. The

table 1 shows the proposed pulse oximeter measured the heart

rate of 14 different subjects and these measured values are

compared with standard pulse oximeter.

Figure 7. System Model for Calculating Heart Rate

Figure 8. Heart rate output in LABVI

TABLE I

COMPARISON OF CALCULATING HEART RATE

SUBJECT

HEART RATE IN BPM (MATLAB)

HEART RATE IN BPM (LABVIEW)

1

69

74

2

73

68

3

81

77

4

57

63

5

84

80

6

80

76

7

68

66

8

63

62

9

69

66

10

85

76

11

52

58

12

82

85

13

87

75

14

82

77

IV CONCLUSION

The bedside monitoring system for measurement of

heart rate from the patient using PPG sensor and accelerometer

is presented in this paper. By using kalman filter, all the noises

are removed and the PPG signals from fourteen healthy

individuals were acquired and their heart rate values were

calculated and analysed in LabVIEW and MATLAB. In future,

the system will be designed to eliminate the motion artifacrts

due to hand movement. The heart rate values will be

transmitted wirelessly to the server when a critical condition

occurs and it will sent alert to the intern person by SMS.

ACKNOWLEDGEMENT

The authors would like to thank Rajasekaran and

Anitha for their suggestions and support. And also thank for

Karunya University for providing the facilites to do the Project.

REFERENCES

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