A NEW ROBUST QRS DETECTION ALGORITHM IN ARRHYTHMIC ECG SIGNALS

Year 2018, , 64 - 73, 26.03.2018

Süleyman Bılgın , Zahide Elif Akın

https://doi.org/10.21923/jesd.391625

Cited By: 3

Abstract

The QRS detection in electrocardiogram (ECG) signals
provides significant information to help automatic diagnosis of some
cardiovascular disorders. There are many studies about QRS detection in the
literature. All these studies have focused on the development of QRS detection including
noise, baseline wander, artifacts, small and wide QRS complexes. However, some
QRS complexes cannot be detected due to their morphological and arrhythmic
disorders. These types of beats are misevaluated during observation. Therefore,
increasing the success and accuracy of such algorithms is of great importance
for the development of wearable cardiac diagnostic devices. Arrhythmic ECG
signals include different morphologic features, such as sudden, narrow, small,
and negative QRS complexes, which are very difficult to automatically detect. In
this study, we propose a new algorithm with higher accuracy than other studies
in the literature for the detection these types of QRS complexes. The proposed
method based on digital filtering and Discrete Wavelet Transform (DWT) is
evaluated and tested using the two-channel ECG records obtained from 48
patients in the MIT/BIH arrhythmia database. The overall performance results of
this algorithm are calculated as 99.79% of the sensitivity, 99.95% of the
predictivity rate, the detection error rate of 0.26 and 99.74% of accuracy score.

Keywords

Electrocardiogram, QRS Detection, Discrete Wavelet Transform, Digital Filters

ARİTMİK EKG SİNYALLERİNDE DAYANIKLI YENİ BİR QRS YAKALAMA ALGORİTMASI

Abstract

Elektrokardiyogram (EKG) sinyallerindeki QRS algılama,
bazı kardiyovasküler bozuklukların otomatik teşhisine yardımcı olmak için
önemli bilgiler sağlamaktadır. Literatürde QRS tespiti ile ilgili birçok
çalışma bulunmaktadır. Tüm bu çalışmalar, elektriksel gürültü, taban hattı
kayması, kas gürültüleri, küçük ve geniş QRS kompleksleri dahil olmak üzere QRS
algılamanın geliştirilmesine odaklanmıştır. Bununla birlikte, bazı QRS
kompleksleri morfolojik ve aritmik bozuklukları nedeniyle tespit edilemez. Bu
vuruş türleri gözlem sırasında yanlış değerlendirilir. Bu nedenle, bu tür
algoritmaların başarısını ve doğruluğunu arttırmak, giyilebilir kalp tanı
cihazlarının geliştirilmesi için büyük önem taşımaktadır. Aritmik EKG
sinyalleri, otomatik olarak algılanması çok zor olan ani, dar, küçük ve negatif
QRS kompleksleri gibi farklı morfolojik özellikleri içerir. Bu çalışmada, bu
tür QRS komplekslerinin saptanması için literatürdeki diğer çalışmalardan daha
yüksek doğrulukta yeni bir algoritma önermekteyiz. Dijital filtrelemeye ve
Ayrık Dalgacık Dönüşümüne (ADD) dayanan bu yöntem MIT / BIH aritmi veri
tabanındaki 48 hastadan elde edilen iki kanallı EKG kayıtlarını kullanarak
değerlendirildi ve test edildi. Bu algoritmanın genel performans sonuçlarında,
duyarlılık %99,79, öngörme oranı %99,95, algılama hata oranı 0,26 ve doğruluk
skoru %99,74 olarak hesaplanmaktadır.

Keywords

Elektrokardiyogram, QRS Yakalama, Ayrık Dalgacık Dönüşümü, Sayısal Süzgeçler

References

• Pan, J., Tompkins, W.J., 1985. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 3, 230-236.
• Paoletti, M., Marchesi, C., 2006. Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis. Computer Methods and programs in biomedicine, 82 (1), 20-30.
• Xue, Q., Hu, Y.H., Tompkins, W.J., 1992. Neural-network-based adaptive matched filtering for QRS detection. IEEE Transactions on Biomedical Engineering, 39 (4), 317-329.
• Zidelmal, Z., Amirou, A., Adnane, M., Belouchrani, A., 2012. QRS detection based on wavelet coefficients. Computer methods and programs in biomedicine, 107 (3), 490-496.
• Chen, S.W., Chen, H.C., Chan, H.L., 2006. A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Computer methods and programs in biomedicine, 82 (3), 187–195.
• Rufas, D.C., Carrabina, J., 2015. Simple real-time QRS detector with the MaMeMi filter. Biomedical Signal Processing and Control, 21, 137-145.
• Yeh, Y.C., Wang, W.J., 2008. QRS complexes detection for ECG signal: The Difference Operation Method. Computer methods and programs in biomedicine, 91 (3), 245-254.
• Moraes, J., Freitas, M., Vilani, F., Costa, E., 2002. A QRS complex detection algorithm using electrocardiogram leads. Conference on Computers in Cardiology, 205-208.
• Manikandan, M.S., Soman, K., 2012. A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomedical Signal Processing and Control, 7 (1), 18-128.
• Farashi, S., 2016. A multiresolution time-dependent entropy method for QRS complex detection. Biomedical Signal Processing and Control, 24, 63-71.
• Sharma, L.D.,, Sunkaria R.K., 2016. A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement, 87, 194-204.
• Arzeno, N.M., Deng, Z.D., Poon, C.S., 2008. Analysis of first-derivative based QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 55 (2), 478-484.
• Chouhan, V., Mehta, S., 2008. Detection of QRS complexes in 12-lead ECG using adaptive quantized threshold. International Journal of Computer Science and Network Security, 8 (1), 155-163.
• Tan, K., Chan, K., Choi, K., 2000. Detection of the QRS complex, P wave and T wave in electrocardiogram. First International Conference on Advances in Medical Signal and Information Processing, 41-47.
• Slimane, Z.E.H., Naït-Ali, A., 2010. QRS complex detection using Empirical Mode Decomposition. Digital Signal Processing, 20 (4), 1221-1228.
• Zhang, F., Lian, Y., 2009. QRS detection based on multi-scale mathematical morphology for wearable ECG devices in body area networks. IEEE Transactions on Biomedical Circuits and Systems, 3 (4), 220–228.
• Köhler, B., Hennig, C., Orglmeister, R., 2003. QRS detection using zero crossing counts. Applied genomics and proteomics, 2 (2), 138-145.
• Yochuma, M., Renaud, C., Jacquir, S., 2016. Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control, 25, 46-52.
• Bahoura, M., Hassani, M., Hubin, M., 1997. DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis. Computer methods and programs in biomedicine, 52 (1), 35-44.
• Phukpattaranont, P., 2015. QRS detection algorithm based on the quadratic filter. Expert Systems with Applications, 42 (11), 4867-4877.
• Hongyan, X., Minsong, H., 2008. A new QRS detection algorithm based on empirical mode decomposition. The 2nd International Conference on Bioinformatics and Biomedical Engineering, 693–696.
• Choi, S., Adnane, M., Lee, G.J., Jang, H., Jiang, Z., Park, H.K., 2010. Development of ECG beat segmentation method by combining lowpass filter and irregular R-R interval checkup strategy. Expert Systems with Applications, 37 (7), 5208–5218.
• Karimipour, A., Homaeinezhad, M.R., Real-time electrocardiogram P-QRST detection-delineation algorithm based on quality-supported analysis of characteristic templates. Computers in biology and medicine, 52, 153–165.
• Bouaziz, F., Boutana, D., Benidir, M., 2014. Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Processing, 8 (7), 774–782.
• Martinez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P., 2004. A wavelet-based ECG delineator: evaluation on standard databases. IEEE transactions on biomedical engineering, 51 (4), 570-581.
• Arbateni, K., Bennia, A., 2014. Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing, 145, 438–450.
• Zhu, H., Dong, J., 2013. An R-peak detection method based on peaks of Shannon energy envelope. Biomedical Signal Processing and Control, 8 (5), 466–474.
• Guzeler, A.C., Bilgin, S., 2016. QRS Complex Detection Algorithm Based on Discrete Wavelet and Hilbert Transform within The ECG Signal Arrhythmia. In 20th National Biomedical Engineering Meeting, 219-224.
• Moody, G.B., Mark, R.G., 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20 (3), 45-50.
• Chakraborty, M., Shreya, D., 2012. Determination of signal to noise ratio of electrocardiograms filtered by band pass and savitzky-golay filters. Procedia Technology, 4, 830-833.
• Manolakis, D., Ingle, V.K., 2012. Applied digital signal processing: theory and practice. Cambridge University Press.