Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions

Abstract

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.

Figure 1

Configuration of contact electrodes for EHG recording.

Figure 2

Influence of motion artifacts on EHG features. 1: non-artifacted EHG signal windows. 2: artifacted EHG signal windows. The feature E ₃ upper quartile value for artifacted EHG signals (Group 2) is out of scale.

Figure 3

(a) EHG signal. (b) TOCO-like signal generated from EHG signal using RMS-based algorithm (grey line) and the unnormalized first statistical moment of the frequency spectrum algorithm (black line). The signal windows with amplitude significantly different from the baseline identified by the automatic contraction detector are marked by grey triangle and black point, respectively. (c)-(d) IUP and TOCO were acquired simultaneously.

Figure 4

Waveform of 9 EHG signal windows identified by automatic contraction detector extracted from the recording session shown in Figure 3 using RMS-based algorithm.

Figure 5

(a) EHG signal. (b) TOCO-like signal generated from EHG signal using RMS-based algorithm (grey line) and the unnormalized first statistical moment of the frequency spectrum algorithm (black line). The signal windows with amplitude significantly different from the baseline identified by the automatic contraction detector are marked by grey triangle and black point, respectively. (c)-(d) IUP and TOCO were acquired simultaneously.