Authors: Nan Lu, Jihong Wang, Isobel McDermott, Steve Thornton, Manu Vatish, Harpal Randeva
Addresses: Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, UK. ' Department of Electronics, Electrical and Computer Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. ' Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK. ' Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK. ' Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK. ' Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
Abstract: Uterine electromyography (EMG) signal has potential for early diagnosis of preterm labour clinically. But it is difficult to differentiate the EMG signal patterns leading to preterm birth. In this paper, the effort has been made to find effective algorithms for extracting the features from uterine EMG signals which can be used to classify the normal term labour from abnormal preterm labour signals. A combined algorithm has been proposed, in which the signal is firstly pre-processed to eliminate the noise and high frequency components. Then, the fractal dimension value along the signal is calculated to identify the abnormal values for distinguishing contraction patterns. Two techniques are employed: phase space reconstruction and singular value decomposition. Finally, the signals are classified using artificial neural network method. The experiment tests indicate that the method can be a choice for solving the problem described but more tests are required to draw final conclusion.
Keywords: uterine electromyography; uterine EMG; wavelet transforms; wavelet packet energy; WPE; artificial neural networks; ANNs; signal feature extraction; signal feature classification; preterm labour diagnosis; preterm birth; uterine activity; human pregnancy.
International Journal of Modelling, Identification and Control, 2009 Vol.6 No.2, pp.136 - 146
Available online: 31 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article