Automated Osborn wave detection system based on wavelet features and neural network
Keywords:Osborn wave, ECG signal, wavelet features, quasi-matched wavelet filtration, principal component analysis, PCA, feed forward multilayer perceptron, neural network training
AbstractAutomated Osborn wave detection system featuring the sensitivity of 94.63% and classification accuracy of 94.58% for the notch and slur types of waves in the cardiac signal has been developed. The quasi-matched wavelet filtering method and the method of principal components were applied for extraction and formation of feature vectors representing the input data of classifier. The error feedforward neural network with topology of a multilayer perceptron was used as a classifier. Signal samplings built on information from the PhysioNet open database of medical signals were used for training, testing and validation of neural network. This study involved the use of 12-lead electrocardiograms of 60 healthy patients aged 17–87. These electrocardiograms formed the basis for creating a database of 14832 signals (9888 with Osborn wave signals of two types and 4944 signals without pathological findings). The proposed approach ensured the classification accuracy exceeding the accuracy of existing techniques.
JUNTTILA, M.J.; SAGER, S.J.; TIKKANEN, J.T.; ANTTONEN, O.; HUIKURI, H.V.; MYERBURG, R.J. “Clinical significance of variants of J-points and J-waves: early repolarization patterns and risk,” Eur. Heart J., v.33, n.21, p.2639-2643, 2012. DOI: https://doi.org/10.1093/eurheartj/ehs110.
ANTZELEVICH, Charles; YAN, Gan-Xin; ACKERMAN, Michael J.; BORGGREFE, Martin; CORRADO, Domenico; GUO, Jihong; GUSSAK, Ihor; HASDEMIR, Can; HORIE, Minoru; HUIKURI, Heikki; MA, Changsheng; MORITA, Hiroshi; NAM, Gi-Byoung; SACHER, Frederic; SHIMIZU, Wataru; VISKIN, Sami; WILDE, Arthur A. M. “J-Wave syndromes expert consensus conference report: Emerging concepts and gaps in knowledge,” EP Europace, v.19, n.4, p.665-694, 2017. DOI: https://doi.org/10.1093/europace/euw235.
CLARK, E.N.; KATIBI, I.; MACFARLANE, P.W. “Automatic detection of end QRS notching or slurring,” J. Electrocardiol., v.47, n.2, p.151-134, 2014. DOI: https://doi.org/10.1016/j.jelectrocard.2013.10.007.
WANG, Y.G.; WU, H.T.; DAUBECHIES, I.; LI, Y.; ESTES, E.H.; SOLIMAN, E.Z. “Automated J wave detection from digital 12-lead electrocardiogram,” J. Electrocardiol., v.48, n.1, p.21-28, 2015. DOI: https://doi.org/10.1016/j.jelectrocard.2014.10.006.
LI, Dengao; BAI, Yanfei; ZHAO, Jumin. “A method for automated J wave detection and characterization based on feature extraction,” Lect. Notes Comput. Sci., v.9196, p.421-433, 2015.
RABINER, L.R.; GOLD, B. Theory and Application of Digital Signal Processing. Prentice-Hall, 1975.
GOLDBERGER, A.L.; AMARAL, Luis A. N.; GLASS, L.; HAUSDORFF, J.M.; IVANOV, P.C.; MARK, R.G.; MIETUS, J.E.; MOODY, G.B.; PENG, Chung-Kang; STANLEY, H.E. “PhysioBank, PhysioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, v.101, n.23, p.e215-e220, 2000. DOI: https://doi.org/10.1161/01.CIR.101.23.e215.
DE LA ROSA, E.; FERNANDEZ, E.A. “Spectral bands analysis of ECG derived signals in Chagasic patients,” Proc. of VI Latin American Congress on Biomedical Engineering CLAIB 2014, 29-31 Oct. 2014, Paraná, Argentina. Springer, 2014, v.49, p.484-487. DOI: https://doi.org/10.1007/978-3-319-13117-7_124.
D’YAKONOV, V.P. Wavelets. From Theory to Practice [in Russian]. Moscow: SOLON-Press, 2002.
AIVAZYAN, S.A.; BUKHSHTABER, V.M.; ENYUKOV, I.S.; MESHALKIN, L.D. Applied Statistics. Classification and Dimension Reduction [in Russian]. Moscow: Finansy i Statistika, 1989.
MASTERS, Timothy. Practical Neural Network Recipes in C++. San Diego, CA: Academic Press, 1993.