用于识别SCG轨迹中收缩复合波的深度学习：一项跨数据集分析
Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis
AI总结：
本文通过跨数据集实验验证深度学习在心震图收缩复合波检测中的有效性，并表明个性化步骤可缓解领域偏移，同时证明融合加速度计与陀螺仪的多通道方法能提升性能。
摘要：
心震图信号在心脏活动分析中是传统心电图的很有前景的替代方案。特别是，收缩复合波被认为是心震图中信息最丰富的部分，因此需要进一步分析。目前检测收缩复合波的最先进解决方案基于深度学习模型，这些模型在开创性研究中已被证明有效。然而，这些解决方案仅在受控场景下进行了测试，只考虑从保持仰卧静止的用户身上采集的干净信号。除此之外，所有这些研究都只考虑来自单一数据集的数据，忽视了跨数据集场景带来的益处和挑战。在这项工作中，进行了一项跨数据集实验分析，同时还考虑了来自真实世界场景的数据。我们的研究结果证明了深度学习解决方案的有效性，同时表明个性化步骤对于对抗领域偏移（即训练数据和测试数据之间数据分布的变化）的重要性。最后，我们展示了多通道方法的优势，利用从加速度计和陀螺仪数据中提取的信息。
英文摘要：
The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of the cardiac activity. In particular, the systolic complex is known to be the most informative part of the seismocardiogram, thus requiring further analysis. State-of-art solutions to detect the systolic complex are based on Deep Learning models, which have been proven effective in pioneering studies. However, these solutions have only been tested in a controlled scenario considering only clean signals acquired from users maintained still in supine position. On top of that, all these studies consider data coming from a single dataset, ignoring the benefits and challenges related to a cross-dataset scenario. In this work, a cross-dataset experimental analysis was performed considering also data from a real-world scenario. Our findings prove the effectiveness of a deep learning solution, while showing the importance of a personalization step to contrast the domain shift, namely a change in data distribution between training and testing data. Finally, we demonstrate the benefits of a multi-channels approach, leveraging the information extracted from both accelerometers and gyroscopes data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
