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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Automatic Recognition of Complete Atrioventricular Activity in Fetal Pulsed-Wave Doppler Signals
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Automatic Recognition of Complete Atrioventricular Activity in Fetal Pulsed-Wave Doppler Signals

机译：胎儿脉冲波多普勒信号中房室活动的自动识别

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摘要

Echocardiography is the gold standard for antenatal cardiological assessment. However, the adoption of this technique is challenging, since it is intrinsically operator-dependent and because of the different confounding factors related to the fetal heart size, the fetal movements and the ultrasound artifacts. Among the different options, fetal echocardiography is widely used, concurring to an early diagnosis of several cardiac pathologies. In this work, a neural network-based algorithm targeted at the identification of the most important features of Doppler fetal echocardiography videos is presented and evaluated on real signals. Compared to other approaches, the proposed algorithm works on a couple of ID signals, representing the pulse-wave Doppler envelope extracted from the video, thus preserving a Iightweight approach. For the validation, a small dataset was created, including recordings from five voluntary pregnant women 21st to 27th gestational week), for a total of 20 records, 10 seconds each. The dataset was annotated by an expert cardiologist in order to identify the epochs of the signal where a complete readable cardiac cycle could be identified. The performance of the method was evaluated through a 5-fold cross-validation. An average accuracy up to 88% was obtained, confirming the validity of the proposed approach and paving the way to future improvements of the technique.
机译：超声心动图是产前心脏病评估的金标准。但是，采用该技术具有挑战性，因为它本质上取决于操作员，并且由于与胎儿心脏大小，胎儿运动和超声伪像有关的不同混杂因素。在不同的选择中，胎儿超声心动图检查被广泛使用，这有助于早期诊断几种心脏疾病。在这项工作中，提出了一种基于神经网络的算法，旨在识别多普勒胎儿超声心动图视频的最重要特征，并在真实信号上进行了评估。与其他方法相比，该算法对两个ID信号起作用，这些ID信号代表从视频中提取的脉冲多普勒包络，因此保留了权重方法。为了进行验证，创建了一个小型数据集，其中包括来自五名志愿孕妇的录音21 st 至27 第 孕周），总共20条记录，每条记录10秒。该数据集由专业的心脏病专家注释，以便识别可以识别完整可读心动周期的信号历元。该方法的性能通过5倍交叉验证进行了评估。获得了高达88％的平均准确度，证实了所提出方法的有效性，并为该技术的未来改进铺平了道路。

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