Fractal feature based ECG arrhythmia classification

Shantanu Raghav*, Amit K. Mishra

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


We propose a method for the classification of ECG arrhythmia using local fractal dimensions of ECG signal as the features to classify the arrhythmic beats. The heart beat waveforms were extracted within a fixed length window around the R-peak of the signal and local fractal dimension is calculated at each sample point of the ECG waveform. The method is based on matching these fractal dimension series of the test ECG waveform to that of the representative ECG waveforms of different types of arrhythmia, by calculating Euclidean distances or by calculating the correlation coefficients. The performance of the classifier was tested on independent MIT-BIH arrhythmia database. The achieved performance is represented in terms of the percentage of correct classification ( found to be 99.49% on an average). The performance was found to be competitive to other published results. The current classification algorithm proved to be a computationally efficient and hence a potential technique for automatic recognition of arrhythmic beats in ECG monitors or Holter ECG recorders.

Original languageEnglish
Title of host publication2008 IEEE Region 10 Conference, TENCON 2008
PublisherIEEE Press
Number of pages5
ISBN (Print)1424424089, 9781424424085
Publication statusPublished - 19 Nov 2008
Externally publishedYes
Event2008 IEEE Region 10 Conference, TENCON 2008 - Hyderabad, India
Duration: 19 Nov 200821 Nov 2008

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


Conference2008 IEEE Region 10 Conference, TENCON 2008
Period19 Nov 200821 Nov 2008


  • Beat classification
  • ECG arrhythmia
  • Fractal dimension


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