Comput Math Methods Med - Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions.

Tópicos

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Resumo

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.

Resumo Limpo

electrohysterographi ehg noninvas techniqu monitor uterin electr activ howev presenc artifact ehg signal may give rise erron interpret make difficult extract use inform record aim work develop automat system segment ehg record distinguish uterin contract artifact first segment perform use algorithm generat tocolik signal deriv ehg detect window signific chang amplitud segment classifi two group artifact nonartifact signal develop classifi total eleven spectral tempor nonlinear featur calcul ehg signal window women first stage labor previous classifi expert combin characterist led highest degre accuraci detect artifact determin result show possibl obtain automat detect motion artifact segment ehg record precis use seven featur propos algorithm classifi togeth compos use tool analyz ehg signal help promot clinic applic techniqu

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