This paper aimed to evaluate the effect of various electrode configurations on applying a convolutional neural network (CNN) to recognize uterine contraction (UC) with Electrohysterogram (EHG) signals. Seven 8‐electrode configurations and thirteen 4‐electrode configurations were selected from the 4 × 4 electrode grid in the Icelandic 16‐electrode EHG database. EHG signals were divided into UC and non‐UC sections of 45 seconds and saved as images. Each 8‐electrode configuration with 7152 images and 4‐electrode configuration with 3576 images were applied to train CNN to recognize UCs. A scoring method was proposed based on the area under the curve (AUC) and the accuracy to evaluate the effect of electrode configurations on recognizing UCs. The EHG signals from the 4 electrodes on the upper left of the uterus showed the best classification performance (AUC = 0.79, Accuracy = 0.72, Score = 2.30).

中文翻译：

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电极配置对子宫电波图识别子宫收缩的影响：使用卷积神经网络的分析

本文旨在评估各种电极配置对应用卷积神经网络（CNN）识别具有子宫电波图（EHG）信号的子宫收缩（UC）的影响。从冰岛16电极EHG数据库的4×4电极网格中选择了7个8电极配置和13个4电极配置。EHG信号分为45秒的UC和非UC部分，并保存为图像。每个具有7152个图像的8电极配置和具有3576个图像的4电极配置都用于训练CNN以识别UC。提出了一种基于曲线下面积（AUC）和准确度的评分方法，以评估电极配置对识别UC的影响。