Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data

Polina Kurtser; Ofer Levi; Vladimir Gontar

doi:10.1007/978-3-642-32909-8_20

Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data

Polina Kurtser, Ofer Levi, Vladimir Gontar

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

Abstract

This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps - logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.

Original language	English
Title of host publication	Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings
Editors	Shigang Yue, Lazaros Iliadis
Pages	193-202
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-32909-8_20
State	Published - 2012
Externally published	Yes
Event	2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012 - Chengdu, China Duration: 26 Oct 2012 → 28 Oct 2012

Publication series

Name	Communications in Computer and Information Science
Volume	311
ISSN (Print)	1865-0929

Conference

Conference	2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012
Country/Territory	China
City	Chengdu
Period	26/10/12 → 28/10/12

Keywords

Artificial Neural Networks
Deterministic chaos
ECG

Access to Document

10.1007/978-3-642-32909-8_20

Cite this

Kurtser, P., Levi, O., & Gontar, V. (2012). Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data. In S. Yue, & L. Iliadis (Eds.), Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings (pp. 193-202). (Communications in Computer and Information Science; Vol. 311). https://doi.org/10.1007/978-3-642-32909-8_20

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title = "Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data",

abstract = "This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps - logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.",

keywords = "Artificial Neural Networks, Deterministic chaos, ECG",

author = "Polina Kurtser and Ofer Levi and Vladimir Gontar",

note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; 2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012 ; Conference date: 26-10-2012 Through 28-10-2012",

year = "2012",

doi = "10.1007/978-3-642-32909-8_20",

language = "אנגלית",

isbn = "9783642329081",

series = "Communications in Computer and Information Science",

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editor = "Shigang Yue and Lazaros Iliadis",

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Kurtser, P, Levi, O & Gontar, V 2012, Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data. in S Yue & L Iliadis (eds), Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings. Communications in Computer and Information Science, vol. 311, pp. 193-202, 2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012, Chengdu, China, 26/10/12. https://doi.org/10.1007/978-3-642-32909-8_20

Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data. / Kurtser, Polina; Levi, Ofer; Gontar, Vladimir.
Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings. ed. / Shigang Yue; Lazaros Iliadis. 2012. p. 193-202 (Communications in Computer and Information Science; Vol. 311).

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

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N2 - This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps - logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.

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Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data

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Keywords

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