Classification of multichannel uterine EMG signals

Prediction of preterm deliveries from EHG signals using machine learning

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.

Detecting missing signals in multichannel recordings by using higher order statistics

In real world applications, a multichannel acquisition system is susceptible of having one or many of its sensors displaced or detached, leading therefore to the loss or corruption of the recorded signals. In this paper, we present a technique for detecting missing or corrupted signals in multichannel recordings. Our approach is based on Higher Order Statistics (HOS) analysis. Our approach is tested on real uterine electromyogram (EMG) signals recorded by 4×4 electrode grid. Results have shown that HOS descriptors can discriminate between the two classes of signals (missing vs. non-missing). These results are supported by statistical analysis using the t-test which indicated good statistical significance of 95% confidence level.

Cross-correlation analysis of multichannel uterine EMG signals

The prevention of preterm labor remains one of the primary goals of obstetric research. One way to achieve this goal effectively is to understand the mechanisms regulating the uterine contractility. Herein, we evaluate the correlation between uterine electrical activities recorded from spatially-distributed regions by calculating the nonlinear regression coefficient. Results have shown that, during pregnancy, the degree of interdependence between signals is very high whereas, at labor, the correlation between the signals decreases remarkably. We conclude that pregnancy is characterized by the presence of few local potential sources dominating the other sources while at the onset of labor, the number of these sources increases remarkably which affects therefore the correlation between the signals.