Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder

• Published in: PloS one Vol. 14; no. 4; p. e0214712
• Main Authors: Chen, Lili, Hao, Yaru, Hu, Xue
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.04.2019; Public Library of Science (PLoS)
• Subjects: Accuracy; Approximation; Area Under Curve; Biology and Life Sciences; Birth; Classification; Classifiers; Coefficients; Computer and Information Sciences; Diagnosis; Electromyography; Electrophysiological Phenomena; Electrophysiology; Engineering and Technology; Entropy; Fault diagnosis; Female; Health aspects; Humans; Infant, Newborn; International conferences; Laboratories; Learning algorithms; Mathematical analysis; Medicine and Health Sciences; Methods; Neural networks; Obstetrical research; Physical Sciences; Pregnancy; Premature birth; Premature Birth - diagnosis; Premature infants; Prenatal diagnosis; Research and Analysis Methods; Risk factors; ROC Curve; Sensitivity and Specificity; Signal processing; Support vector machines; Unsupervised Machine Learning; Uterine contractions; Wavelet Analysis; Wavelet transforms; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0214712

Based on electrohysterogram, this paper designed a new method using wavelet-based nonlinear features and stacked sparse autoencoder for preterm birth detection. For each sample, three level wavelet decomposition of a time series was performed. Approximation coefficients at level 3 and detail coefficients at levels 1, 2 and 3 were extracted. Sample entropy of the detail coefficients at levels 1, 2, 3 and approximation coefficients at level 3 were computed as features. The classifier was constructed based on stacked sparse autoencoder. In addition, stacked sparse autoencoder was further compared with extreme learning machine and support vector machine in relation to their classification performance of electrohysterogram. The experiment results reveal that classifier based on stacked sparse autoencoder showed better performance than the other two classifiers with an accuracy of 90%, a sensitivity of 92%, a specificity of 88%. The results indicate that the method proposed in this paper could be effective for detecting preterm birth in electrohysterogram and the framework designed in this work presents higher discriminability than other techniques.