  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.
