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:[email protected]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.
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DEVELOPMENT OF HEART RATE MONITOR
USING PHOTOPLETHYSMOGRAPH Anju Annie Jacob, Mr.R.Jegan
Dept of Electronics and instrumentation engineering Karunya University, Coimbatore, India
SSRG International Journal of Industrial Engineering - (ICRTECITA-2017) -Special issue- March 2017
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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[2] Sagar Venkatesh Gubbi and Bharadwaj, “Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry”, IEEE transactions on biomedical circuits and systems, vol. 9, no. 2, April 2015.
[3] Z. Zhang, Z. Pi, and B. Liu, “TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise,” IEEE transactions on biomedical Engineering, vol. 62, no. 2, February 2015.
[4] Z. Zhang, “Heart rate monitoring from wrist-type photoplethysmographic (PPG) signals during intensive physical exercise,” in Proc.IEEE Global Conf. Signal Inf. Process. (GlobalSIP),Dec. 2014.
[5] M. R. Ram, K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy, “A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter,” IEEE Transactions on Instrumentation & Measurement., vol. 61, no. 5, May 2012.
[6] M. R. Ram, K. V. Madhav, E. H. Krishna, K. N. Reddy, and K. A. Reddy,“On the performance of time varying step-size least mean squares (TVSLMS) adaptive filter for MA reduction from PPG signals,” in Proc. IEEE Int. Conf. Commun. Signal Process, Feb. 2011, pp. 431–435.
[7] M. R. Ram, K. V. Madhav, E. H. Krishna, K. N. Reddy, and K. A. Reddy,“On the performance of AS-LMS based adaptive filter for reduction of motion artifacts from PPG signals,” in Proc. 28th I2MTC, Hangzhou, China, May 10–12, 2011, pp. 1536–1539.
[8] M. R. Ram, K. V. Madhav, E. H. Krishna, K. N. Reddy, and K. A. Reddy,“Adaptive reduction of motion artifacts from PPG signals using a synthetic noise reference signal,” in Proc. IEEE EMBS Conf. Biomed. Eng. Sci., Nov. /Dec. 2010, pp. 315–319.
[9] M. Raghuram, K. V. Madhav, E. H. Krishna, and K. A. Reddy,“Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals,” in Proc. 10th Int. Conf. Inf. Sci. Signal Process. Appl. (ISSPA), May 2010, pp. 460–463.
[10] B. Lee, J. Han, H. J. Baek, J. H. Shin, K. S. Park, and W. J. Yi, “Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry,”Physiol. Meas., vol. 31, no. 12, p. 1585, 2010.
[11] Laxmi Shaw, Sangeeta Bagha,“A Real Time Analysis Of PPG Signal For Measurement of Spo2 And Pulse Rate” International Journal Volume 36– No.11, December 2011.