Electrohysterographic conduction velocity estimation.
Monitoring and analysis of the fetal-heart and the uterine-muscle activity, referred to as electrohysterogram (EHG), is essential to permit timely treatment during pregnancy. While remarkable progress is reported for monitoring of the fetal cardiac activity, the EHG measurement and interpretation remains challenging, and limited knowledge is available on the underlying physiological processes. In particular, little attention has been paid to the analysis of the EHG propagation, whose characteristics might indicate the presence of coordinated uterine contractions leading to intrauterine pressure increase. Therefore, this study focuses for the first time on the noninvasive estimation of the conduction velocity of EHG action potentials by means of multichannel EHG recording and surface high-density electrodes. A maximum likelihood algorithm, initially proposed for skeletal-muscle electromyog-raphy, is modified for the required EHG analysis. The use of clustering and weighting is introduced to deal with poor signal similarity between different channels. The presented methods were evaluated by specific simulations, proving the combination of weighting and clustering to be the most accurate method. A preliminary EHG measurement during labor confirmed the feasibility of the method. An extensive clinical validation will however be necessary to optimize the method and assess the relevance of the EHG conduction velocity for pregnancy monitoring.
Mischi
M
M
Faculty of Electrical Engineering, Eindhoven University of Technology, the Netherlands. m.mischi@tue.nl
Rabotti
C
C
Vosters
L J
LJ
Oei
S G
SG
Bergmans
J M
JM
D000465:Algorithms; D001698:Biomedical Engineering; D004576:Electromyography / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007743:Labor, Obstetric / Q000502:physiology; D016013:Likelihood Functions; D011247:Pregnancy; D012815:Signal Processing, Computer-Assisted; D014590:Uterine Contraction / Q000502:physiology*; D018590:Uterine Monitoring / Q000706:statistics & numerical data*