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The linear synchronization measures of uterine EMG signals: Evidence of synchronized action potentials during propagation
- Source :
- Theriogenology. 86:1873-1878
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Evaluation of synchronization between myoelectric signals can give new insights into the functioning of the complex system of porcine myometrium. We propose a model of uterine contractions according to the hypothesis of action potentials similarity which is possible to detect during propagation in the uterine wall. We introduce similarity measures based on the concept of synchronization as used in matching linear signals such as electromyographic (EMG) time series data. The aim was to present linear measures to assess synchronization between contractions in different topographic regions of the uterus. We use the cross-correlation function (ƒx,y[l], ƒy,z[l]) and the cross-coherence function (Cxy[ƒ], Cyz[ƒ]) to assess synchronization between three data series of a diestral uterine EMG bundles in porcine reproductive tract. Spontaneous uterine activity was recorded using telemetry method directly by three-channel transmitter and three silver bipolar needle electrodes sutured on different topographic regions of the reproductive tract in the sow. The results show the usefulness of the cross-coherence function in that synchronization between uterine horn and corpus uteri for multiple action potentials (bundles) could be observed. The EMG bundles synchronization may be used to investigate the direction and velocity of EMG signals propagation in porcine reproductive tract.
- Subjects :
- 0301 basic medicine
Swine
TOPOGRAPHIC REGIONS
Reproductive tract
Action Potentials
Data series
Uterine Contraction
03 medical and health sciences
Food Animals
Synchronization (computer science)
Animals
Small Animals
Mathematics
Uterine activity
Electromyography
Equine
business.industry
Uterus
Myometrium
Uterine horns
Pattern recognition
Anatomy
Action (physics)
030104 developmental biology
Female
Animal Science and Zoology
Artificial intelligence
business
Subjects
Details
- ISSN :
- 0093691X
- Volume :
- 86
- Database :
- OpenAIRE
- Journal :
- Theriogenology
- Accession number :
- edsair.doi.dedup.....2beb895d0afa657549af2d964d648561
- Full Text :
- https://doi.org/10.1016/j.theriogenology.2016.03.036