  Automated sleep stage classification using heart-rate variability is an
active field of research. In this work limitations of the current
state-of-the-art are addressed through the use of deep learning techniques and
their efficacy is demonstrated. First, a temporal model is proposed for the
inference of sleep stages from electrocardiography using a deep long- and
short-term (LSTM) classifier and it is shown that this model outperforms
previous approaches which were often limited to non-temporal or Markovian
classifiers on a comprehensive benchmark data set (292 participants, 541214
samples) comprising a wide range of ages and pathological profiles, achieving a
Cohen's $\kappa$ of $0.61\pm0.16$ and accuracy of $76.30\pm10.17$ annotated
according to the Rechtschaffen & Kales annotation standard.
  Subsequently, it is demonstrated how knowledge learned on this large
benchmark data set can be re-used through transfer learning for the
classification of photoplethysmography (PPG) data. This is done using a smaller
data set (60 participants, 91479 samples) that is annotated with the more
recent American Association of Sleep Medicine annotation standard, achieving a
Cohen's $\kappa$ of $0.63\pm0.13$ and accuracy of $74.65\pm8.63$ for
wrist-mounted PPG-based sleep stage classification, higher than any previously
reported performance using this sensor modality. This demonstrates the
feasibility of knowledge transfer in sleep staging to adapt models for new
sensor modalities as well as different annotation strategies.
