Diagnosing Dangerous Arrhythmia of Patients by Automatic Detecting of QRS Complexes in ECG
In this paper, an automatic detecting algorithm for
QRS complex detecting was applied for analyzing ECG recordings
and five criteria for dangerous arrhythmia diagnosing are applied for a
protocol type of automatic arrhythmia diagnosing system. The
automatic detecting algorithm applied in this paper detected the
distribution of QRS complexes in ECG recordings and related
information, such as heart rate and RR interval. In this investigation,
twenty sampled ECG recordings of patients with different pathologic
conditions were collected for off-line analysis. A combinative
application of four digital filters for bettering ECG signals and
promoting detecting rate for QRS complex was proposed as
pre-processing. Both of hardware filters and digital filters were
applied to eliminate different types of noises mixed with ECG
recordings. Then, an automatic detecting algorithm of QRS complex
was applied for verifying the distribution of QRS complex. Finally,
the quantitative clinic criteria for diagnosing arrhythmia were
programmed in a practical application for automatic arrhythmia
diagnosing as a post-processor. The results of diagnoses by automatic
dangerous arrhythmia diagnosing were compared with the results of
off-line diagnoses by experienced clinic physicians. The results of
comparison showed the application of automatic dangerous
arrhythmia diagnosis performed a matching rate of 95% compared
with an experienced physician-s diagnoses.
[1] Rissam HS; Kishore S; Srivastava S; Bhatia ML; Trehan N AUTHOR,
Evaluation of cardiac symptoms by trans-telephonic electrocardiographic
monitoring (TTEM): preliminary experience. Indian
Heart J 50(1):55-8, Jan-Feb 1998.
[2] I.K. Daskalov, I.I. Christov. Electrocardiogram signal preprocessing for
automatic detection of QRS . Med Eng & Phys, 37-44, 1999.
[3] M. Bahoura, M. Hassani, M. Hubin, DSP implementation of wavelet
transform for real time ECG wave forms detection and heart rate
analysis, computer method & programs in biomedicine, 52: 35-44 , 1997.
[4] P. Jiapu and W. J. Tompkins., A Real-Time QRS Detection Algorithm,
IEEE trans. on bio-medical engineering, 32(3): 230-236, March 1985.
[5] J. Lee, K. Jeong, J. Yoon; M. Lee, A Simple Real-Time QRS Detection
Algorithm, 18th Annual Intern. Conference of the IEEE Engineering in
Medicine and Biology Society, 4: 1396-1398, Nov. 1996.
[6] Saeid Reza Seydnejad and Richard I. Kitney, Real-Time Heart Rate
Variability Extraction Using The Kaiser Window, IEEE Transactions on
Biomedical Engineering, 44: 990-1005, Oct. 1997.
[7] G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and
H. T. Nagle,A Comparison of the Noise Sensitivity of Nine QRS
Detection Algorithms, IEEE Transactions on Biomedical Engineering,
37(1): 85-98, Jan 1990.
[8] Barbara Aehlert, RN, ECGs MADE EASY 2/e, Elsevier (Singapore) Pte
Ltd.,2003.
[9] J. Enderle, S. Blanchard, and J. Bronzino, Introduction to Biomedical
Engineering, Academic Press, 2000.
[10] R. Ganguli, Noise and Outlier Removal from Jet Engine Health Signals
using Weighted FIR Median Hybrid Filters, Mechanical Systems and
Signal Processing, 16(6) 967-978, 2002.
[11] G. Rizzoni, Principles and Applications of Electrical Engineering,
2nd.ed, The Ohio State University, 1996.
[1] Rissam HS; Kishore S; Srivastava S; Bhatia ML; Trehan N AUTHOR,
Evaluation of cardiac symptoms by trans-telephonic electrocardiographic
monitoring (TTEM): preliminary experience. Indian
Heart J 50(1):55-8, Jan-Feb 1998.
[2] I.K. Daskalov, I.I. Christov. Electrocardiogram signal preprocessing for
automatic detection of QRS . Med Eng & Phys, 37-44, 1999.
[3] M. Bahoura, M. Hassani, M. Hubin, DSP implementation of wavelet
transform for real time ECG wave forms detection and heart rate
analysis, computer method & programs in biomedicine, 52: 35-44 , 1997.
[4] P. Jiapu and W. J. Tompkins., A Real-Time QRS Detection Algorithm,
IEEE trans. on bio-medical engineering, 32(3): 230-236, March 1985.
[5] J. Lee, K. Jeong, J. Yoon; M. Lee, A Simple Real-Time QRS Detection
Algorithm, 18th Annual Intern. Conference of the IEEE Engineering in
Medicine and Biology Society, 4: 1396-1398, Nov. 1996.
[6] Saeid Reza Seydnejad and Richard I. Kitney, Real-Time Heart Rate
Variability Extraction Using The Kaiser Window, IEEE Transactions on
Biomedical Engineering, 44: 990-1005, Oct. 1997.
[7] G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and
H. T. Nagle,A Comparison of the Noise Sensitivity of Nine QRS
Detection Algorithms, IEEE Transactions on Biomedical Engineering,
37(1): 85-98, Jan 1990.
[8] Barbara Aehlert, RN, ECGs MADE EASY 2/e, Elsevier (Singapore) Pte
Ltd.,2003.
[9] J. Enderle, S. Blanchard, and J. Bronzino, Introduction to Biomedical
Engineering, Academic Press, 2000.
[10] R. Ganguli, Noise and Outlier Removal from Jet Engine Health Signals
using Weighted FIR Median Hybrid Filters, Mechanical Systems and
Signal Processing, 16(6) 967-978, 2002.
[11] G. Rizzoni, Principles and Applications of Electrical Engineering,
2nd.ed, The Ohio State University, 1996.
@article{"International Journal of Medical, Medicine and Health Sciences:63865", author = "Jia-Rong Yeh and Ai-Hsien Li and Jiann-Shing Shieh and Yen-An Su and Chi-Yu Yang", title = "Diagnosing Dangerous Arrhythmia of Patients by Automatic Detecting of QRS Complexes in ECG", abstract = "In this paper, an automatic detecting algorithm for
QRS complex detecting was applied for analyzing ECG recordings
and five criteria for dangerous arrhythmia diagnosing are applied for a
protocol type of automatic arrhythmia diagnosing system. The
automatic detecting algorithm applied in this paper detected the
distribution of QRS complexes in ECG recordings and related
information, such as heart rate and RR interval. In this investigation,
twenty sampled ECG recordings of patients with different pathologic
conditions were collected for off-line analysis. A combinative
application of four digital filters for bettering ECG signals and
promoting detecting rate for QRS complex was proposed as
pre-processing. Both of hardware filters and digital filters were
applied to eliminate different types of noises mixed with ECG
recordings. Then, an automatic detecting algorithm of QRS complex
was applied for verifying the distribution of QRS complex. Finally,
the quantitative clinic criteria for diagnosing arrhythmia were
programmed in a practical application for automatic arrhythmia
diagnosing as a post-processor. The results of diagnoses by automatic
dangerous arrhythmia diagnosing were compared with the results of
off-line diagnoses by experienced clinic physicians. The results of
comparison showed the application of automatic dangerous
arrhythmia diagnosis performed a matching rate of 95% compared
with an experienced physician-s diagnoses.", keywords = "Signal processing, electrocardiography (ECG), QRS
complex, arrhythmia.", volume = "2", number = "3", pages = "116-7", }