Detection of electrocardiographic changes in partial epileptic patients using local binary pattern based composite feature

Detection of electrocardiographic changes in partial epileptic patients using local binary... In this paper, we propose a novel method for detecting electrocardiographic (ECG) changes in partial epileptic patients using a composite feature set. At the core of our approach is a local binary pattern (LBP) based feature representation containing a set of statistical features derived from the distribution of LBPs of the ECG signal. In order to enhance the discriminating power, a set of statistical features are also extracted from the original ECG signal. The composite feature is then generated by combining the two homogeneous feature sets. The discriminating ability of the proposed composite feature is investigated using two different classifiers namely, support vector machine and a bagged ensemble of decision trees. Results from the experimental evaluation on the publicly available MIT-BIH ECG dataset demonstrate the superiority of the proposed features over conventional histogram based LBP features. Our results also show that the proposed approach provides better classification accuracy than methods existing in the literature for classification of normal and partial epileptic beats in ECG. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australasian Physical & Engineering Sciences in Medicine Springer Journals

Detection of electrocardiographic changes in partial epileptic patients using local binary pattern based composite feature

Loading next page...
 
/lp/springer_journal/detection-of-electrocardiographic-changes-in-partial-epileptic-Vxov0MCaUc
Publisher
Springer Journals
Copyright
Copyright © 2017 by Australasian College of Physical Scientists and Engineers in Medicine
Subject
Biomedicine; Biomedicine, general; Biological and Medical Physics, Biophysics; Medical and Radiation Physics; Biomedical Engineering
ISSN
0158-9938
eISSN
1879-5447
D.O.I.
10.1007/s13246-017-0605-8
Publisher site
See Article on Publisher Site

Abstract

In this paper, we propose a novel method for detecting electrocardiographic (ECG) changes in partial epileptic patients using a composite feature set. At the core of our approach is a local binary pattern (LBP) based feature representation containing a set of statistical features derived from the distribution of LBPs of the ECG signal. In order to enhance the discriminating power, a set of statistical features are also extracted from the original ECG signal. The composite feature is then generated by combining the two homogeneous feature sets. The discriminating ability of the proposed composite feature is investigated using two different classifiers namely, support vector machine and a bagged ensemble of decision trees. Results from the experimental evaluation on the publicly available MIT-BIH ECG dataset demonstrate the superiority of the proposed features over conventional histogram based LBP features. Our results also show that the proposed approach provides better classification accuracy than methods existing in the literature for classification of normal and partial epileptic beats in ECG.

Journal

Australasian Physical & Engineering Sciences in MedicineSpringer Journals

Published: Nov 30, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off