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Authors: Rolf Vetter 1 ; Jonas Schild 1 ; Annette Kuhn 2 and Lorenz Radlinger 3

Affiliations: 1 Bern University of Applied Siences, Switzerland ; 2 Bern University Hospital and University of Bern, Switzerland ; 3 Bern University of Applied Sciences, Switzerland

ISBN: 978-989-758-069-7

Keyword(s): Wavelet, Autoregressive Modelling, Patient Discrimination, Pelvic Floor Muscle.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing ; Wavelet Transform

Abstract: Rehabilitation therapies to treat female stress urinary incontinence focus on the reactivation of pelvic floor muscle (PFM) activity. An objective measure is essential to assess a subject’s improvement in PFM capabilities and increase the success rate of the therapy. In order to provide such a measure, we propose a method for the discrimination of healthy subjects with strong PFM and post-partum subjects with weak PFM. Our method is based on a dyadic discrete wavelet decomposition of electromyograms (EMG) that projects slow-twitched and fast-twitched muscle activities onto different scales. We used a parametric auto-regressive (AR) model for the estimation of the frequency of each wavelet scale to overcome the poor frequency resolution of the dyadic decomposition. The feature used for discrimination was the frequency of the wavelet scale with the highest variance after interpolation with the nearest neighboring scales. Twenty-three healthy and 26 post-partum women with weak PFM wh o executed 4 maximum voluntary contractions (MVC) were retrospectively analysed. EMGs were recorded using a vaginal probe. The proposed method has a lower rate of false discrimination (4%) compared to the two classical methods based on mean (9%) and median (7%) frequency estimation from the power spectral density. (More)

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Paper citation in several formats:
Vetter, R.; Schild, J.; Kuhn, A. and Radlinger, L. (2015). Discrimination of Healthy and Post-partum Subjects using Wavelet Filterbank and Auto-regressive Modelling.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 132-137. DOI: 10.5220/0005176301320137

@conference{biosignals15,
author={Rolf Vetter. and Jonas Schild. and Annette Kuhn. and Lorenz Radlinger.},
title={Discrimination of Healthy and Post-partum Subjects using Wavelet Filterbank and Auto-regressive Modelling},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={132-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005176301320137},
isbn={978-989-758-069-7},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Discrimination of Healthy and Post-partum Subjects using Wavelet Filterbank and Auto-regressive Modelling
SN - 978-989-758-069-7
AU - Vetter, R.
AU - Schild, J.
AU - Kuhn, A.
AU - Radlinger, L.
PY - 2015
SP - 132
EP - 137
DO - 10.5220/0005176301320137

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