J. Parak, J. Havlik
Abstract
Digital signal processing and data analysis are very often used methods in a biomedical engineering research. This paper describes utilization of digital signal filtering on electrocardiogram (ECG). Designed filters are focused on removing supply network 50 Hz frequency and breathing muscle artefacts. Moreover, this paper contains description of three heart rate frequency detection algorithms from ECG. Algorithms are based on statistical and differential mathematical methods. All of the methods are compared on stress test measurements. All described methods are suitable for next simple implementation to a microprocessor for real-time signal processing and analysing
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Introduction
Electrocardiogram (ECG) represents electrical activity of human heart. ECG is composite from 5 waves - P, Q, R, S and T. This signal could be measured by electrodes from human body in typical engagement. Signals from these electrodes are brought to simple electrical circuits with amplifiers and analogue – digital converters. The main problem of digitalized signal is interference with other noisy signals like power supply network 50 Hz frequency and breathing muscle artefacts. These noisy elements have to be removed before the signal is used for next data processing like heart rate frequency detection. Digital filters and signal processing should be designed very effective for next real-time applications in embedded devices. Heart rate frequency is very important health status information. The frequency measurement is used in many medical or sport applications like stress tests or life treating situation prediction. One of possible ways how to get heart rate frequency is compute it from the ECG signal. Heart rate frequency can be detected d from ECG signal by many methods and algorithms. Many algorithms for heart rate detection are based on QRS complex detection and hear rate is computed like distance between QRS complexes. QRS complex can be detected using for example algorithms from the field of artificial neural networks, genetic algorithms, wavelet transforms or filter banks [1]. Moreover the next way how to detect QRS complex is to use adaptive threshold [2]. The direct methods for heart rate detection are ECG signal spectral analyse [3] and Short-Term Autocorrelation method [4]. Disadvantage of all these methods is their complicated implementation to microprocessor unit for real time heart rate frequency detection. Real time QRS detector and heart rate computing algorithm from resting 24 hours ECG signal for 8-bit microcontroller is described in [5]. This algorithm is not designed for physical stress test with artefacts. The designed digital filters and heart rate frequency detection algorithms are very simple but robust. They can be used for ECG signal processing during physical stress test with muscle artefacts. They are suitable for easy implementation in C language to microprocessor unit in embedded device. Design of these methods has been very easy with Matlab tools and functions.
Result
The designed digital filters and the heart rate frequency algorithms are very simple. The filters have small order. It saves the computing time, but it is very effective for processing the ECG signal. It is the reason why these algorithms could be easy implemented to microprocessor unit. Based on the application of the computing algorithms to digitally filtrated ECG signal which was acquired during the stress test it may be argued that differential computing methods are better for real-time processing implementation.
Acknowledgements
This work has been supported by the grant No. F3a 2122/2011 presented by University Development Foundation. This work has been also supported by the research program No. MSM 6840770012 of the Czech Technical University in Prague (sponsored by the Ministry of Education, Youth and Sports of the Czech Republic).
References
[1] Kohler, B.-U.; Hennig, C.; Orglmeister, R. The principles of software QRS detection. Engineering in Medicine and Biology Magazine IEEE, vol. 21, pp. 42 – 57, January -February 2002.
[2] I. I. Christov. Real time electrocardiogram QRS detection using combined adaptive threshold. BioMedical Engineering OnLine, 2004. [cit: 2011-10-16]. [Online]. Available on internet: http://www.biomedical-engineering-online.com/content/3/1/28.
[3] Surda, J.; Lovas, S.; Pucik, J.; Jus, M. Spectral Properties of ECG Signal. Radioelektronika, 2007. 17th International Conference, Brno, Czech Republic, 24th – 25th April 2007, pp. 1 – 5.
[4] Piotrowskia Z.; Rózanowski K. Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation. ACTA PHYSICA POLONICA, vol. 118, pp. 131 – 135, No. 1/2010.
[5] Pan, Jiapu; Tompkins, Willis J. A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, vol. BME-22, pp. 230 – 236, March 2005.
[6] Tenedero, M. C.; Raya, D. A. M..; Sison, G. L. Design and implementation of a single-channel ECG amplifier with DSP post-processing in Matlab. Third National Electronics & Engineering Conference, Phillipines, November 2002.
[7] Danek, J. CARDIOLOGY, ECG – basic information. BTL, a. s., March 2010. [cit: 2011-10-16]. [Online].Available on internet:http://noel.feld.cvut.cz/vyu/x31let/Lectures/05_EKGDanek.pdf.
[8] Jacobson, A.L. Auto-threshold peak detection in physiological signals. 23rd Annual International Conference of the IEEE, 25th – 28th October 2001, vol. 3, pp. 2194 – 2195.
[9] Nygårds, M.; Sörnmo, L. Delineation of the QRS complex using the envelope of the e.c.g. Medical and Biological Engineering and Computing, vol. 21, pp. 538 – 547, September 1983.
[10] Billauer, E. peakdet: Peak detection using MATLAB. September, 2008. [cit: 2011-10-16]. [Online]. Available on internet: http://www.billauer.co.il/peakdet.html.