Volume 6 Number 7 (Jul. 2011)
Home > Archive > 2011 > Volume 6 Number 7 (Jul. 2011) >
JSW 2011 Vol.6(7): 1257-1264 ISSN: 1796-217X
doi: 10.4304/jsw.6.7.1257-1264

Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering

Gang Zheng1, 2, 3, Tian Yu1, 2
1Laboratory of Biological signal and Intelligent Processing, Tianjin University of Technology, Tianjin, China
2Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China
3School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin, China

Abstract—A hybrid strategy has been proposed to reduce the wrong clustering on Ambulatory ECG (electrocardiogram). Since Ambulatory ECG is usually composed by 24 hours data, the number of individual ECG waveform can reach to 100,000, the request for accurate clustering result is highly required. The proposed strategy adopted some intelligent algorithms to solve the above problem. It clusters ECG waveform sample (selected from Ambulatory ECG) synchronously by Max-Min distance clustering algorithm, K-means algorithm and Simulated annealing algorithm first. And then, it adopted all three outputs from the above three algorithms as input on Back Propagation Artificial Neural Network (BP ANN). In the end, we got more accurate clustering result from the output of ANN. For testing the results, data of MIT/BIH arrhythmia database were used for experiments. After the controlled trial on MIT/BIH data, it can be safely concluded that the clustering result achieved by improved strategy can got more accurate than that by the traditional clustering algorithm. An average accuracy ratio is about 94.6%, 1.6% higher than k-means algorithm averagely and 1.3% higher than Simulated Annealing algorithm averagely.

Index Terms—ambulatory electrocardiogram, k-means, artificial neural networks, clustering algorithm, Simulated Annealing algorithm


Cite: Gang Zheng, Tian Yu, "Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering," Journal of Software vol. 6, no. 7, pp. 1257-1264, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
  • Apr 26, 2021 News!

    Vol 14, No 4- Vol 14, No 12 has been indexed by IET-(Inspec)     [Click]

  • Jun 22, 2020 News!

    Papers published in JSW Vol 14, No 1- Vol 15 No 4 have been indexed by DBLP     [Click]

  • Sep 13, 2021 News!

    The papers published in Vol 16, No 6 have all received dois from Crossref    [Click]

  • Jan 28, 2021 News!

    [CFP] 2021 the annual meeting of JSW Editorial Board, ICCSM 2021, will be held in Rome, Italy, July 21-23, 2021   [Click]

  • Sep 13, 2021 News!

    Vol 16, No 6 has been published with online version     [Click]