Microcontroller Based RR-Interval Measurement Using PPG Signals for Heart Rate Variability based Biometric Application Nazneen Akhter, Sumegh Tharewal, Hanumant Gite, K. V. Kale Department of Computer Science and Information Technology Dr. Babasaheb Ambedkar Marathwada University Aurangabad (Maharashtra) India
[email protected],
[email protected],
[email protected],
[email protected]
Abstract— Heart Rate Variability (HRV) is a natural property of heart rate. Medical science since last two decades has been viewing at it as a diagnostic and prognostic tool. This study is intended towards harnessing the HRV property of heart for person identification. The highest peak in the ECG signal as well as PPG signal as seen in Figure 1, is known as the R-peak, while the time duration between two adjacent R-peak is known as RRInterval. RR-Intervals are the only requirement for HRV analysis. Traditionally it is measured from an Electrocardiography (ECG) signals, but we used photoplethysmography (PPG) based pulse sensor and in-house designed microcontroller based RR-Interval measurement system. PPG sensors come in two basic types, one uses transmission and the other one makes use of reflection. We have tested the hardware with both transmission and reflection type sensors. This article is intended to document the performance analysis of both types of PPG sensors. And also present results of biometric identification based on RR-Intervals collected at the fingertips. Classification is done using KNN classifier.
equipped with the classical printer (parallel) port, therefore the design is improvisation to our previous work which employed parallel port of PC [1]. This design provides additional features and can be connected to computer through serial port or USB port (using a serial to USB bridge), this makes the system more flexible and versatile. Details of design and construction and performance analysis of three types of PPG signal based pulse sensors are presented and its use in biometric application is also presented.
Keywords— Heart Rate Variability; Photoplethysmography (PPG); Pulse Sensor, HRV analysis; Poincare Map; K-Nearest Neighbor Classifier; Biometric Identification
I.
INTRODUCTION
Research and development in embedded system and microcontroller based instrumentation resulted in a boom with brisk activity in different areas of engineering, science and technology including the field of life sciences. Parameters of interest related to Heart Rate Variability (HRV) were difficult to quantitatively determine, monitor and analyze and needed sophisticated dedicated instrumentation. Measurement of related variables and parameters has been much facilitated due to the advances and progress in electronic instrumentation and this proved to be very useful in diagnosis and evaluation. Sophisticated and dedicated instruments are already being employed for monitoring critical patients, however the need of simple and inexpensive techniques that can be afforded by individuals or primary health care centers or research laboratories is also being strongly felt. With this in view we developed an RRI measurement and data acquisition system that can be interfaced with a computer using serial or USB port. New generation desktop PC’s and laptops are not
c 978-1-4799-8792-4/15/$31.00 2015 IEEE
Figure 1: ECG and PPG signals indicating the RR-Intervals between two consecutive heart beats.
II.
BRIEF BACKGROUND
The highest peak in the ECG signal as well as PPG signal as seen in Figure 1, is known as the R-peak, while the time duration between two adjacent R-peak is known as RRInterval. RR-Intervals are the duration between two consecutive heart beats as seen in Figure 1, this duration exhibits variability as the RR-Interval is found to change appreciably from beat to beat. Only RR-Interval is required for HRV analysis and it is traditionally measured from ECG signals. Researchers have documented evidences in favor of PPG to surrogate ECG for HRV analysis [1-3]. PPG based detectors use the mechanical activity of the beating of the heart
588
to identify a beat in contrast to ECG where electrical activity is monitored. ECG from the chest is the clearest, but rarely used outside hospital [4] and if it has to be employed in biometric applications it faces the challenge of poor user cooperation. If heart signals are to be used in biometric recognition systems, then other methods need to be explored. PPG sensors being low cost and comfortable in data collection are one of the instant choices for ECG alternative. S. Israel et al. in [5] gave an extensive performance analysis of three different sensing methods of heart i.e. ECG, pulse oximetry and blood pressure, which documented the latter two methods to be on the lower side. Da Silva, H.P. et al. in [6] has presented the usability and performance study of heart signals from fingertips and also in [7] DA Silva, H.P. et al. proposed a new off the person dataset of ECG data collected at fingertips, which strengthens the reliability of heart signals collected at the fingertips. PPG based RR-Interval measurement is promising in the present application because of simplicity and ease of use.
measuring the cardiovascular heartbeat wave that is found all through the human body especially at the peripherals like fingertip toes or ear lobes. The beat wave is brought about by the rhythmic throbs of blood vessels in synchronism with the heartbeat, causing changes in the flow of blood and the beat is detected from the changing optical density or opacity.
III. SYSTEM DESCRIPTION For the purpose of quantitative measurement of the RR-interval (RRI) to study, the Heart Rate Variability (HRV), a microcontroller (Atmega32 from AVR family) based sensing and Data Acquisition System (DAS) is designed and implemented. The data acquisition and computer interface part very much resemble the system used for recording real time parameters during fractal growth [8] and is radical modification and improvisation of an earlier design [1], few limitations like data size restriction and sampling time resolution are taken care of and improvised. The proposed system consists of three main units, i.e. The Pulse Sensor, the controlling unit and the Acquisition Software described in the following sections. A. Pulse Sensors
Figure 3: Pulse Sensor working on the principle of Reflectance
Initially we employed a commercially available sensor for heartbeat detection as seen in Figure 3, the sensor unit was based on the reflection of bright red light through muscles and tissues. It consisted of a source of bright red light (LED) and a detector of light mounted on one platform. It had built in signal processing and pulse shaping circuitry. When finger is placed on the detector unit, the reflected light is detected by the sensor and amplified. As the real signal (change in amount of reflected light) is small, it is superimposed on a large background illumination. The detector unit subtracts this background and shapes the pulse through the onboard microcontroller and finally presents a square wave TTL pulse of 100 ms duration at the output. This signal was found fairly consistent and was used in the preliminary experiments. The performance of the measuring system along with the microcontroller based timing unit was good and reliable data was recorded and analyzed.
Figure 4: Pulse Sensor working on the principle of Transmission Figure 2: Pulses Sensors experimented for reliable RR-Interval Measurement.
The pulse sensors used in our system as seen in Figure 2 works on the PPG signals which is actually an electro-optic method of
At a certain stage of developmental work, we decided to change the heartbeat pulse detection part due to certain limitations discussed in Section IV, and tried a finger strip type
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
589
of sensor as shown in Figure 4 that came with its own signal processing unit like the earlier one, however, in this case the pulse conditioning electronics was an independent module connecting through a 9 pin D type shell connector to the source and detector pair on the strip that can be wound around a finger. There were a few advantages of this, the first and the most important being that the limitation of the 10 ms in RR interval measurement was removed and additionally it allowed for the ease of use as the subject was not supposed to hold the finger at the sensor undisturbed over the entire period of recording of the RR interval. The sensor used transmission of red light through the finger and detected the R peaks based on the signal received and the source and sensor were mounted in a flexible fabric to wrap around the finger. This made it somewhat delicate and the sensor assembly developed loose contacts resulting in failure repeatedly. Also the output of the sensor assembly was an analogue output that was compatible with TTL circuit of the controlling microcontroller based circuit, however insertion of a fixed threshold monostable
Figure 6: Circuit Diagram of the Controlling Unit
This microcontroller based control unit was constructed on general purpose AVR microcontroller development board with necessary power supply. The microcontroller used was Atmega32 running at a crystal frequency of 11.0592 MHz, keeping in view the serial port communication. The system was provided with in system programming (ISP) facility so that the microcontroller firmware can easily be modified right in the working conditions. The real time RR interval readings were displayed on a four line 20 column LCD display interfaced with the Port B, the LCD was configured in 4 bit data mode and six lines from this port PB0 – PB5 were used.
Figure 5: Pulse Sensor working on the principle of Reflectance
improved the satisfactorily.
performance
and
the
system
worked
Finally we resorted to a reliable sensor device using transmission of IR radiation through finger or earlobe as seen in Figure 5. This sensor was used with the earlier signal processing electronics module to obtain reliable performance. As the sensor was based on IR detection, it was immune to the changes of ambient illumination providing an additional advantage. This combination worked well with the whole setup and was used for most of the work. B. Controlling Unit The circuit diagram of the control unit of the RR interval acquisition system as seen in Figure 6 was designed and developed to measure the RR interval on a real time basis from the pulses received from the heartbeat detection system. The RR interval was measured in milliseconds which may range from 500 to 1500 ms (corresponding to a pulse rate of 120 to 40 respectively) and the range was far more than sufficient for normal healthy subjects. This RR interval was sent as serial data to a computer side controlling program where it was received, decoded and saved for further processing.
590
Figure 7: GUI of Acquisition Software
The heartbeat pulses from the sensor were connected to port pin PD4 that was configured as an input port and the microcontroller firmware polled this pin for incoming pulses. The interval between two consecutive peaks was measured in milliseconds and sent to serial port. As the data size was more than 8 bit (in fact 11 bits) it was split into 2 bytes of data. The serial port was configured in RS 232 mode with 8 bits of data and one start and one stop bit with no parity. The two bytes of data was sent serially one by one byte that was received by the computer side program and decoded accordingly. C. Acquisition Software
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
The data acquisition system of Figure 7 is the computer side program written in visual basic 6 that collects the RR-Intervals and stores in text files for further processing. This is developed in Visual Basic with GUI support for ease of operation in a user friendly manner and displays the real time RR-Interval received in a graphic panel. It receives the RR data sent by the device via USB port in the form of two bytes and performs some preprocessing like combining of two bytes and saving the results in text files for further use. Screenshot in Figure 7 shows a typical data collection for 512 intervals of a subject. The GUI consists of four modes of collecting RR-Intervals i.e. for 1 minute, 2 minutes, 5 minutes and 10 minutes, in the first mode, 64 RR-Intervals are measured, and for the rest of the three modes, it records 256, 512, and 1024 intervals respectively. The computer side program has provision to record data from different subjects under different conditions and store in appropriate folders for further analysis. It also has the capability to auto detect and remove ectopic beats, i.e. noise from the HRV data. Ectopy here is nothing but noise or unwanted data that needs either elimination or correction. Details of ectopia removal are presented in [9]. IV. SENSOR’S PERFORMANCE ANALYSIS
words, the resolution of the sensor unit was 10 ms and all the points falling in the interval of 10 ms are represented by a
Figure 9: Analogue signal as seen on Oscilloscope
single point. This was one of the drawbacks of the reliable heartbeat sensor discussed above, to overcome this; we attempted different configurations and combination and settled with another heartbeat sensor shown in Figure 4. This sensor works on the principle of changing the transmission of red light through muscles and tissues during a heartbeat. When the heart beats, the flow of blood through the capillaries is increased which in turn results in increased opaqueness in red light. This sensor connects to the signal processing circuit, that is part of the sensor unit via a 9 pin D type shell connector. The sensor unit is provided with 5V DC supply and gives output in the form of analogue signals. The analogue signal is 2 – 3V in height and thus can directly be used by TTL circuitry like a microcontroller system, however such interfacing is prone to
Figure 8: Poincare Map of RR Intervals Collected Using Sensor of Figure 3
Typical Poincare plot presented in Figure 8 represents RRI data obtained using the heartbeat sensor shown in Figure 3. It was observed that most of the points cluster together at one location because of this overlapping of points the number of points visible in the plot are much less than the actual number of points. Additionally the plot does not reflect the actual variability of the heartbeat data and the plot looks like a symmetric grid. The RR interval recorded by this system has an inherent property that the data points are not continuously in real time but time step is fixed. On investigation, it was revealed that the time resolution of the detector assembly itself is 10 ms and it cannot distinguish a time difference of less than this. This feature was highly undesirable as the study relates to the actual RR interval and its variations to be used as a characteristic of the individual. Closer examination of the plot revealed that the data points are separated by a time that is multiple of 10 ms In other
Figure 10: Pulse shaping mono-stable circuit is seen well in synchronism with the analogue output
jitter because of the levels of triggering. The yellow traces (Channel – 1) in Figure 9 i.e. the oscilloscope screenshot shows
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
591
the analogue signal that is provided by the sensor unit as its standard output. To eliminate this disadvantage, we used a simple monostable constructed using a timer IC NE555 giving approximately 200 ms pulses when triggered. Figure 10 shows the analogue output of the sensor along with the square, TTL output of the mono-stable, for ease of comparison the TTL signal is inverted in the display. The TTL output of the pulse shaping mono-stable circuit is well in synchronism with the analogue output of the sensor unit. This system performed well with the microcontroller unit and provided reliable waveform and the limitation of 10 ms in the measurement of the inter beat interval or the RR interval was removed. The microcontroller unit had a resolution of 1 ms that was found to be sufficient for the present application. With this improvement on the hardware side it was possible to visualize more clearly the variability using Poincare plot as shown in the Figure 11. In this plot, the clustering of points is eliminated and individual points are distinctly visible. The signal processing part of this sensor module was much better and reliable, however the Red light transmitting LED and detecting components were mounted on a fabric band that was getting deteriorated with use causing loose contacts making the whole system delicate and fragile. Also ambient illumination hinders with the red light and measurements based on reflection or transmission of red light. Therefore, we went for an IR source detector pair that was specifically designed for heartbeat measurement using finger or earlobe in figure 5. This sensor did not have signal processing circuitry of its own, we borrowed this part of the signal processing circuit from the white sensor discussed above and retained the mono-
V BIOMETRIC APPLICATION There have been several classification attempts for disease pattern identification in HRV data [10-13]. But only two early attempts of recognition using HRV are documented in literature. A. Milliani et al. in [14] attempted to recognize two different postures i.e. upright and supine of each individual using HRV, but basically their focus was more on identification of posture individual by individual and not specifically biometric recognition, while J. Irvine et al. [15] proposed HRV based human identification which is the only reported attempt specifically aimed at biometric recognition but its techniques and results are unknown due to lack of information. As HRV data consist of strong person specific patterns, which have been so far used for disease classification, encouraged by the fact we are exploring the possibility of person identification in HRV data. A. Database At present our database consists of 2430 sequences of RRIntervals of 81 subjects (47 males and 34 Females) whose 10 samples each of 64 RR-Intervals were measured continuously for 1 minute approximately, in three different sessions. B. Feature Set Generation In an attempt to use the HRV in biometric application, we generated the HRV parameters as suggested in [16, 17]. These HRV parameters are used by researchers for classification of diseases. We call these parameters as HRV features that can also serve as a feature vector for biometric classification. In all we generated 20(seven from time-domain & 13 from frequency domain using Welch algorithm) HRV features from the RRI sequences. C. Classification We randomly selected 40 subjects from the database. We used the data samples from session 1 & 3 in the present experiment. Five samples from session 1 and five from session 3. We utilized six samples for training purpose and 4 for testing. We use 1-Nearest Neighbor classifier for the classification purpose. At k= 1 the system gave very encouraging results with a 92.26% recognition rate. As this was just testing the of the system and feature set, we didn’t experiment with various values of K. Detailed Biometric identification experimentation with feature selection and application of two different classifiers on HRV features is presented in [18].
Figure 12: Comparison of analogue signal from the IR transmitter along with the signal conditioning circuitry.
stable circuit for pulse shaping. This arrangement is performing reliably for a long time as the source detector clip is sturdy. Figure 12 shows a comparison of the analogue signal from the IR transmitter receiver type detection unit along with the signal from conditioning circuitry. The analogue output of the assembly is near square wave with an initial hump and slight taper in the main peak. This is fully taken care by the monostable that provides reliable TTL pulses to be directly connected to the microcontroller circuit i.e. the port pins.
592
VI CONCLUSION We successfully implemented a simple, cost effective and easy to use RR interval acquisition system using a commercially available heartbeat sensor based on IR sensor and detector. The hardware part included microcontroller based control unit communicating with the computer, thereby recording real time RR interval. From the three types of sensors tested the sensor using IR transmission through the muscles and tissues for detection of a heartbeat was found to be most convenient and reliable, however others were also consistent in performance except for on that had a limitation of time resolution to 10 ms. The entire system was thoroughly
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
tested and the information cross checked using alternate methods. The data acquired using the setup discussed was used in biometric application for person classification using KNN classifier and was found to be excellent. This setup is intended to go with a larger setup using Fusion in multimodal biometric system VII FUTURE WORK Right now the presented framework is utilized for RRInterval acquisition for HRV feature generation so that it can be used as a feature vector in biometric applications. However, HRV is having the capability of becoming a prognostic and diagnostic tool, this medical dimension needs to explored and prospective studies needs to be designed and implemented. The same framework with little modification can be utilized as a part of telemedicine framework too, this is one exceptionally encouraging future direction. ACKNOWLEDGEMENT This work was carried out in Multimodal System Development laboratory established under UGC’s SAP scheme, SAP (II) DRS Phase-I F. No.-3-42/2009 & SAP (II) DRS Phase-II F. No.4-15/2015. This work was also supported by UGC under One Time Research Grant F. No. 4-10/2010 (BSR) & 19-132/2014 (BSR).The authors acknowledge UGC for the same and also for providing BSR fellowships. REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10] C. Lin, J.-S. Wang, and P. Chung, “Mining Physiological Conditions from Heart Rate Variability Analysis,” IEEE Computational Intelligence Magazine, vol. 5, no. 1. pp. 50–58, 2010. [11] P. Melillo, “Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 3, pp. 727 – 733, 2013. [12] S. Nizami, J. R. Green, J. M. Eklund, and C. McGregor, “Heart disease classification through HRV analysis using parallel cascade identification and fast orthogonal search,” in 2010 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2010 – Proceedings pp. 134–139, 2010. [13] M. Szypulska and Z. Piotrowski, “Prediction of Fatigue and Sleep Onset Using HRV Analysis,” in Mixed Design of Integrated Circuits and Systems (MIXDES), Proceedings of the 19th International Conference pp. 543–546, 2012. [14] A Malliani, M. Pagani, R. Furlan, S. Guzzetti, D. Lucini, N. Montano, S. Cerutti, and G. S. Mela, “Individual recognition by heart rate variability of two different autonomic profiles related to posture.,” Circulation, vol. 96, pp. 4143–4145, 1997. [15] J.M. Irvine, B.K.Wiederhold, L.W. Gavshon, S.A. Israel, S.B., McGehee, R. Meyer, M.D. Wiederhold, Heart rate variability:a new biometric for human identification, in: International Conference on Artificial Intelligence (IC-AI’2001), pp. 1106–1111, 2001. [16] AHA and ESC, “Guidelines Heart rate variability,” Eur. Heart J., pp. 354–381, 1996. [17] U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: A review,” Med. Biol. Eng. Comput., vol. 44, no. 12, pp. 1031–1051, 2006. [18] Nazneen Akhter, Sumegh Tharewal, Vijay Kale, Ashish Bhalerao, K. V. Kale, “Heart Based Biometrics and Heart Rate Variability in Human Identification Systems, 2nd International Doctoral Symposium on Applied Computation and Security Systems, 2015.
N. Akhter, J. F. Mahdi, G. R. Manza, and D. Registrar, “Microcontroller based data acquisition system for Heart Rate Variability ( HRV ) measurement,” vol. 1, no. 4, pp. 576–583, 2012. W.-H. Lin, D. Wu, C. Li, H. Zhang, and Y.-T. Zhang, “Comparison of Heart Rate Variability from PPG with That from ECG,” in The International Conference on Health Informatics SE - 54, vol. 42, Y.-T. Zhang, Ed. Springer International Publishing, 2014, pp. 213–215. N. Selvaraj, A. Jaryal, J. Santhosh, K. K. Deepak, and S. Anand, “Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography,” J. Med. Eng. Technol., vol. 32, no. 6, pp. 479–484, Jan. 2008. E. G. and M. O. and R. B. and J. M. V. and L. M. and P. Laguna, “Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions,” Physiol. Meas., vol. 31, no. 9, p. 1271, 2010. S. a. Israel, J. M. Irvine, B. K. Wiederhold, and M. D. Wiederhold, “The Heartbeat: The Living Biometric,” Biometrics Theory, Methods, Appl., pp. 429–459, 2009. H. P. da Silva, A. Fred, A. Lourenco, and A. K. Jain, “Finger ECG signal for user authentication: Usability and performance,” Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on. pp. 1–8, 2013. H. P. da Silva, A. Lourenço, A. Fred, N. Raposo, and M. Aires-deSousa, “Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics,” Comput. Methods Programs Biomed., vol. 113, pp. 503–514, 2014. S. Yusuf. H, A R. Khan, and S. H. Behere, “AVR Microcontroller Based Data Acquisition System for Laboratory Experiments,” vol. 3, no. 1, pp. 208–215, 2012. Nazneen Akhter, Hanumnat Gite, Sumegh Tharewal, K. V. Kale, “Computer Based RR-Interval Detection System with Ectopy Correction in HRV Data”, Innovations in Information, Embedded and Communication systems (ICIIECS), IEEE 2nd International Conference on. 2015,
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
593
