Computer vision methods typically optimize for first-order dynamics (e.g., optical flow). However, in many cases the properties of interest are subtle variations in higher-order changes, such as acceleration. This is true in the cardiac pulse, where the second derivative can be used as an indicator of blood pressure and arterial disease. Recent developments in camera-based vital sign measurement have shown that cardiac measurements can be recovered with impressive accuracy from videos; however, the majority of research has focused on extracting summary statistics such as heart rate. Less emphasis has been put on the accuracy of waveform morphology that is necessary for many clinically impactful scenarios. In this work, we provide evidence that higher-order dynamics are better estimated by neural models when explicitly optimized for in the loss function. Furthermore, adding second-derivative inputs also improves performance when estimating second-order dynamics. By incorporating the second derivative of both the input frames and the target vital sign signals into the training procedure, our model is better able to estimate left ventricle ejection time (LVET) intervals.
翻译:计算机视觉方法通常最优化于一阶动态(例如光学流)。然而,在许多情况下,人们感兴趣的特性是更高阶变化的细微变化,例如加速度。这在心脏脉搏中确实如此,第二衍生物可以用作血压和动脉疾病的指标。基于相机的重要标志测量的最近发展显示,通过视频的精确度可以令人印象深刻地恢复心脏测量;然而,大多数研究侧重于提取心率等简要统计数据。对于许多临床影响性情景所必需的波形形态的准确性,重点不那么强调。在这项工作中,我们提供证据表明,在损失功能中明确优化时,神经模型可以更好地估计较高级动态。此外,在估计二阶动态时,增加二阶值投入也会提高性能。通过将输入框架和目标关键标志信号的第二衍生物纳入培训程序,我们的模型能够更好地估计左脑喷射时间间隔。