Cathelain Guillaume, Rivet Bertrand, Achard Sophie, Bergounioux Jean, Jouen Francois
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:465-468. doi: 10.1109/EMBC44109.2020.9176687.
Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.g. respiratory amplitude modulation and body position shifts). We propose a new heartbeat detection method using neural networks to learn this variability. A U-Net model takes thirty-second-long records as inputs and acts like a nonlinear filter. For each record, it outputs the samples probabilities of belonging to IJK segments. A heartbeat detection algorithm finally detects heartbeats from those segments, based on a distance criterion. The U-Net has been trained on 30 healthy subjects and tested on 10 healthy subjects, from 8 to 74 years old. Heartbeats have been detected with 92% precision and 80% recall, with possible optimization in the future to achieve better performance.
监测新生儿的生命体征可能有害并导致发育问题。心冲击图法是一种非接触式心率监测方法，有潜力减轻这种监测带来的痛苦。然而，由于噪声、固有的生理变异性和伪影（如呼吸幅度调制和身体位置移动），信号处理并不容易。我们提出了一种使用神经网络来学习这种变异性的新心跳检测方法。一个U-Net模型将30秒长的记录作为输入，并起到非线性滤波器的作用。对于每个记录，它输出属于IJK段的样本概率。最后，一种心跳检测算法基于距离标准从这些段中检测心跳。U-Net已在30名健康受试者上进行训练，并在10名年龄从8岁到74岁的健康受试者上进行测试。心跳检测的精确率为92%，召回率为80%，未来可能通过优化实现更好的性能。
