This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS) in multi-channel electrical impedance-based hemodynamic monitoring. The proposed method attempts to tackle shortcomings in existing learning-based assessment approaches, such as the requirement of manual annotation for motion influence and the lack of explicit mechanisms for realizing motion-induced abnormalities under contextual variations in CVS over time. By utilizing long-short term memory and variational auto-encoder structures, an encoder--decoder model is trained not only to self-reproduce an input sequence of the CVS but also to extrapolate the future in a parallel fashion. By doing so, the model can capture contextual knowledge lying in a temporal CVS sequence while being regularized to explore a general relationship over the entire time-series. A motion-influenced CVS of low-quality is detected, based on the residual between the input sequence and its neural representation with a cut--off value determined from the two-sigma rule of thumb over the training set. Our experimental observations validated two claims: (i) in the learning environment of label-absence, assessment performance is achievable at a competitive level to the supervised setting, and (ii) the contextual information across a time series of CVS is advantageous for effectively realizing motion-induced unrealistic distortions in signal amplitude and morphology. We also investigated the capability as a pseudo-labeling tool to minimize human-craft annotation by preemptively providing strong candidates for motion-induced anomalies. Empirical evidence has shown that machine-guided annotation can reduce inevitable human-errors during manual assessment while minimizing cumbersome and time-consuming processes.

翻译：本研究提出了一种无监督序列到序列学习方法，可自动评估多通道电阻抗血流动力学监测中心脏容积信号(CVS)因运动引起的可靠性退化。该方法旨在解决现有基于学习的评估方法中的不足，例如需要手动标注运动影响，以及缺乏在CVS随时间变化的情境中实现运动诱发异常的显式机制。通过利用长短期记忆网络和变分自编码器结构，编码器-解码器模型不仅被训练用于自我复现CVS的输入序列，还能以并行方式外推未来状态。通过这种方式，模型能够捕获时间CVS序列中的上下文知识，同时通过正则化探索整个时间序列的通用关系。基于输入序列与其神经表示之间的残差，结合由训练集上的两西格玛经验法则确定的截止值，可检测到受运动影响的低质量CVS。我们的实验观察验证了两个观点：(i) 在标签缺失的学习环境中，评估性能可达到与有监督设置相竞争的水平；(ii) 跨CVS时间序列的上下文信息有助于有效识别运动引起的信号幅度和形态的非真实失真。我们还探究了该方法作为伪标注工具的潜力，通过预先提供运动诱发异常强候选来最小化人工标注。实证证据表明，机器引导的标注可减少人工评估中不可避免的人为错误，同时最小化繁琐且耗时的过程。