ECG compression and labview implementation
Affiliation(s)
GRIET, JNTU, Hyderabad, India, Member IETE;. JNTU, Hyderabad, India, Member IEEE;. GRIET, Hyderabad, India..
GRIET, JNTU, Hyderabad, India, Member IETE;. JNTU, Hyderabad, India, Member IEEE;. GRIET, Hyderabad, India..
ABSTRACT
It is often very difficult for the patient to tell the difference between angina symptoms and heart attack symptoms, so it is very important to recognize the signs of heart attack and immedi-ately seek medical attention. A practical case of this type of remote consultation is examined in this paper. To deal with the huge amount of electrocardiogram (ECG) data for analysis, storage and transmission; an efficient ECG compression technique is needed to reduce the amount of data as much as possible while pre-serving the clinical significant signal for cardiac diagnosis. Here the ECG signal is analyzed for various parameters such as heart rate, QRS-width, etc. Then the various parameters and the compressed signal can be transmitted with less channel capacity. Comparison of various ECG compression techniques like TURNING POINT, AZTEC, CORTES, FFT and DCT it was found that DCT is the best suitable compression technique with compression ratio of about 100:1. In addition, different techniques are available for implementation of hardware components for signal pickup the virtual im-plementation with labview is also used for analysis of various cardiac parameters and to identify the abnormalities like Tachycardia, Bradycardia, AV Block, etc. Both hardware and virtual implementation are also detailed in this context.
It is often very difficult for the patient to tell the difference between angina symptoms and heart attack symptoms, so it is very important to recognize the signs of heart attack and immedi-ately seek medical attention. A practical case of this type of remote consultation is examined in this paper. To deal with the huge amount of electrocardiogram (ECG) data for analysis, storage and transmission; an efficient ECG compression technique is needed to reduce the amount of data as much as possible while pre-serving the clinical significant signal for cardiac diagnosis. Here the ECG signal is analyzed for various parameters such as heart rate, QRS-width, etc. Then the various parameters and the compressed signal can be transmitted with less channel capacity. Comparison of various ECG compression techniques like TURNING POINT, AZTEC, CORTES, FFT and DCT it was found that DCT is the best suitable compression technique with compression ratio of about 100:1. In addition, different techniques are available for implementation of hardware components for signal pickup the virtual im-plementation with labview is also used for analysis of various cardiac parameters and to identify the abnormalities like Tachycardia, Bradycardia, AV Block, etc. Both hardware and virtual implementation are also detailed in this context.
Cite this paper
nullPadma, T. , Latha, M. and Ahmed, A. (2009) ECG compression and labview implementation. Journal of Biomedical Science and Engineering, 2, 177-183. doi: 10.4236/jbise.2009.23030.
nullPadma, T. , Latha, M. and Ahmed, A. (2009) ECG compression and labview implementation. Journal of Biomedical Science and Engineering, 2, 177-183. doi: 10.4236/jbise.2009.23030.
References
[1] S. Jalaleddine, C. Hutchens, R. Stratan, and W. A. Coberly, (1990) ECG data compression techniques-a unified approach, IEEE Trans. Biomed. Eng., 37, 329-343. [2] J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, (1968) AZTEC, a preprocessing program for real time ECG rhythm analysis, IEEE Trans. Biomed. Eng., BME-15, 129-129. [3] B. R. S. Reddy and I. S. N. Murthy, (1986) ECG data compression using fourier descriptors, IEEE Trans. Biomed. Eng., BME-33, 428-433. [4] D. C. Reddy, (2007) Biomedical signal processing-principles and techniques, 254-300, Tata McGraw-Hill, Third reprint. [5] J. L. Simmlow, Biosignal and biomedical image processing- MATLAB based applications, 4-29. [6] A. S. Al-Fahoum, (2006) Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure, IEEE Trans. in Biomedicine, 10, 182-191. [7] V. Kumar, S. C. Saxena, and V. K. Giri, (2006) Direct data compression of ECG signal for telemedicine, ICSS , 10, 45-63. [8] A. Perkusich, G. S. Deep, M. L. B. Perkusich, and M. L. Varani, (1989) An expert ECG acquisition and analysis system, IMTC- 89, 184-189.
[1] S. Jalaleddine, C. Hutchens, R. Stratan, and W. A. Coberly, (1990) ECG data compression techniques-a unified approach, IEEE Trans. Biomed. Eng., 37, 329-343. [2] J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, (1968) AZTEC, a preprocessing program for real time ECG rhythm analysis, IEEE Trans. Biomed. Eng., BME-15, 129-129. [3] B. R. S. Reddy and I. S. N. Murthy, (1986) ECG data compression using fourier descriptors, IEEE Trans. Biomed. Eng., BME-33, 428-433. [4] D. C. Reddy, (2007) Biomedical signal processing-principles and techniques, 254-300, Tata McGraw-Hill, Third reprint. [5] J. L. Simmlow, Biosignal and biomedical image processing- MATLAB based applications, 4-29. [6] A. S. Al-Fahoum, (2006) Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure, IEEE Trans. in Biomedicine, 10, 182-191. [7] V. Kumar, S. C. Saxena, and V. K. Giri, (2006) Direct data compression of ECG signal for telemedicine, ICSS , 10, 45-63. [8] A. Perkusich, G. S. Deep, M. L. B. Perkusich, and M. L. Varani, (1989) An expert ECG acquisition and analysis system, IMTC- 89, 184-189.