Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:662-665. doi: 10.1109/EMBC48229.2022.9871477.
Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.
心跳检测是心脏信号分析的一个重要组成部分，因为它被认为是心脏功能的代表性指标。心跳检测的金标准是在心电图中定位 QRS 复合波。由于传感器和信息与通信技术（ICT）的发展，地震心动描记术（SCG）正在成为监测心率的心电图的可行替代方法。在这项工作中，我们提出了一种使用深度学习方法基于地震心动描记术检测心跳的系统。该研究使用了一个公开可用的数据集（CEBS），该数据集包含心电图、呼吸信号和地震心动描记术的同步测量。我们在地震心动描记术中的心跳检测方法使用了基于 ResNet 的卷积神经网络模型，并包含一个挤压和激励单元。我们的模型在测试数据集上取得了最先进的结果（Jaccard 和 F1 得分均高于 97%），证明了其高度可靠性。
