Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

Abstract

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

“…The results present a strong case for oversampling and indicate that better predictive models are possible for predicting term and preterm records. In summary, the results are better than [4], [12] and [13]. As it can be seen from Table 6, the LMNC, BPXNC and the RBNC have produced the best AUC, Sensitivity, and Specificity values, with low filter mean error rates.…”

“…This paper uses the same dataset as [4] and [7] to generate the four features (root mean square, median frequency, peak frequency and sample entropy)used in the experiment. The raw uterine EHG signal has been extracted from Physionet [20], using the WFDB tool.…”

“…This approach has seen some interesting results that suggest it may be an interesting line of enquiry to pursue. EHG is used to measure electrical activity in the uterus, while machine learning algorithms are trained to distinguish between term and preterm EHG records through the detection of patterns in data, while managing variance between subjects [3], [4]. This paper focuses on using advanced artificial neural network classification techniques, using EHG signals, to detect the onset of preterm births.…”

Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth

“…The features extracted after the prefiltering are root mean square, variance, log detector, mean frequency, median frequency, peak frequency, spectral moment, frequency ratio, approximate entropy, sample entropy, maximal Lyapnov exponent, etc. [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The classification methods used in conventional methods include the artificial neural network, K-nearest, decision tree, Parzan classifier, etc.…”

“…The classification methods used in conventional methods include the artificial neural network, K-nearest, decision tree, Parzan classifier, etc. [6,11,13,20,24,25]. Fig.…”