[Research on Malignant Arrhythmia Detection Algorithm Using Neural Network Optimized by Genetic Algorithm]

Overview

Journal Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

Specialty Biomedical Engineering

Date 2018 May 11

PMID 29745509

Citations 1

Authors

Ming Yu

Feng Chen

Guang Zhang

Liangzhe Li

Chunchen Wang

Ningbo Zhan

Biao Gu

Jing Wei

Taihu Wu

Affiliations

Soon will be listed here.

Abstract

Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.

Citing Articles

[Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting].

Yan H, Mo S, Huang H, Liu Y Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021; 38(5):848-857.

PMID: 34713652 PMC: 9927429. DOI: 10.7507/1001-5515.202101054.

References

Li Q, Mark R, Clifford G . Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas. 2008; 29(1):15-32. PMC: 2259026. DOI: 10.1088/0967-3334/29/1/002. View

Li Q, Rajagopalan C, Clifford G . Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng. 2013; 61(6):1607-13. DOI: 10.1109/TBME.2013.2275000. View

Mozaffarian D, Benjamin E, Go A, Arnett D, Blaha M, Cushman M . Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2015; 133(4):e38-360. DOI: 10.1161/CIR.0000000000000350. View

Link M, Berkow L, Kudenchuk P, Halperin H, Hess E, Moitra V . Part 7: Adult Advanced Cardiovascular Life Support: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015; 132(18 Suppl 2):S444-64. DOI: 10.1161/CIR.0000000000000261. View

Zhang X, Zhu Y, Thakor N, Wang Z . Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng. 1999; 46(5):548-55. DOI: 10.1109/10.759055. View

Jekova I, Krasteva V . Real time detection of ventricular fibrillation and tachycardia. Physiol Meas. 2004; 25(5):1167-78. DOI: 10.1088/0967-3334/25/5/007. View

Thakor N, Zhu Y, Pan K . Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng. 1990; 37(9):837-43. DOI: 10.1109/10.58594. View

Alonso-Atienza F, Morgado E, Fernandez-Martinez L, Garcia-Alberola A, Rojo-Alvarez J . Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng. 2013; 61(3):832-40. DOI: 10.1109/TBME.2013.2290800. View

Amann A, Tratnig R, Unterkofler K . Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng. 2007; 54(1):174-7. DOI: 10.1109/TBME.2006.880909. View

10.

Amann A, Tratnig R, Unterkofler K . Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online. 2005; 4:60. PMC: 1283146. DOI: 10.1186/1475-925X-4-60. View

11.

Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov P, Mark R . PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101(23):E215-20. DOI: 10.1161/01.cir.101.23.e215. View

12.

Figuera C, Irusta U, Morgado E, Aramendi E, Ayala U, Wik L . Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators. PLoS One. 2016; 11(7):e0159654. PMC: 4956226. DOI: 10.1371/journal.pone.0159654. View

13.

Chen S, Thakor N, MOWER M . Ventricular fibrillation detection by a regression test on the autocorrelation function. Med Biol Eng Comput. 1987; 25(3):241-9. DOI: 10.1007/BF02447420. View

14.

Barro S, Ruiz R, Cabello D, Mira J . Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng. 1989; 11(4):320-8. DOI: 10.1016/0141-5425(89)90067-8. View

15.

Jekova I . Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol Meas. 2000; 21(4):429-39. DOI: 10.1088/0967-3334/21/4/301. View

16.

Clayton R, Murray A, CAMPBELL R . Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG. Med Biol Eng Comput. 1993; 31(2):111-7. DOI: 10.1007/BF02446668. View

17.

Clayton R, Murray A, CAMPBELL R . Recognition of ventricular fibrillation using neural networks. Med Biol Eng Comput. 1994; 32(2):217-20. DOI: 10.1007/BF02518922. View