Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

15Citations
Citations of this article
27Readers
Mendeley users who have this article in their library.

Abstract

Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance. © 2012 Moslem et al.; licensee Springer.

References Powered by Scopus

An overview of statistical learning theory

5449Citations
N/AReaders
Get full text

Statistical pattern recognition: A review

5092Citations
N/AReaders
Get full text

Multisensor data fusion

347Citations
N/AReaders
Get full text

Cited by Powered by Scopus

On the Use of Knitted Antennas and Inductively Coupled RFID Tags for Wearable Applications

112Citations
N/AReaders
Get full text

Characterization and automatic classification of preterm and term uterine records

59Citations
N/AReaders
Get full text

Deep neural network for semi-automatic classification of term and preterm uterine recordings

25Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Moslem, B., Diab, M., Khalil, M., & Marque, C. (2012). Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals. Eurasip Journal on Advances in Signal Processing, 2012(1). https://doi.org/10.1186/1687-6180-2012-167

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 10

56%

Researcher 5

28%

Professor / Associate Prof. 2

11%

Lecturer / Post doc 1

6%

Readers' Discipline

Tooltip

Engineering 15

71%

Computer Science 3

14%

Medicine and Dentistry 2

10%

Economics, Econometrics and Finance 1

5%

Save time finding and organizing research with Mendeley

Sign up for free