Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images

Eileen M. McMahon; Josef Korinek; Honghai Zhang; Milan Sonka; Armando Manduca; Marck Belohlavek

doi:10.1109/IJCNN.2005.1556406

Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images

Eileen M. McMahon, Josef Korinek, Honghai Zhang, Milan Sonka, Armando Manduca, Marck Belohlavek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classification by both Principal Component Analysis (PCA) and by Neural Network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	3017-3022
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1556406
State	Published - 2005
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: Jul 31 2005 → Aug 4 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	5

Other

Other	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	7/31/05 → 8/4/05

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2005.1556406

Cite this

McMahon, E. M., Korinek, J., Zhang, H., Sonka, M., Manduca, A., & Belohlavek, M. (2005). Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 3017-3022). Article 1556406 (Proceedings of the International Joint Conference on Neural Networks; Vol. 5). https://doi.org/10.1109/IJCNN.2005.1556406

Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images. / McMahon, Eileen M.; Korinek, Josef; Zhang, Honghai et al.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 3017-3022 1556406 (Proceedings of the International Joint Conference on Neural Networks; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

McMahon, EM, Korinek, J, Zhang, H, Sonka, M, Manduca, A & Belohlavek, M 2005, Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1556406, Proceedings of the International Joint Conference on Neural Networks, vol. 5, pp. 3017-3022, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 7/31/05. https://doi.org/10.1109/IJCNN.2005.1556406

McMahon EM, Korinek J, Zhang H, Sonka M, Manduca A , Belohlavek M. Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 3017-3022. 1556406. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2005.1556406

McMahon, Eileen M. ; Korinek, Josef ; Zhang, Honghai et al. / Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. pp. 3017-3022 (Proceedings of the International Joint Conference on Neural Networks).

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AB - We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classification by both Principal Component Analysis (PCA) and by Neural Network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.

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Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images

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