  Objective: To develop multisensor-wearable-device sleep monitoring algorithms
that are robust to health disruptions affecting sleep patterns. Methods: We
develop an unsupervised transfer learning algorithm based on a multivariate
hidden Markov model and Fisher's linear discriminant analysis, adaptively
adjusting to sleep pattern shift by training on dynamics of sleep/wake states.
The proposed algorithm operates, without requiring a priori information about
true sleep/wake states, by establishing an initial training set with hidden
Markov model and leveraging a taper window mechanism to learn the sleep pattern
in an incremental fashion. Our domain-adaptation algorithm is applied to a
dataset collected in a human viral challenge study to identify sleep/wake
periods of both uninfected and infected participants. Results: The algorithm
successfully detects sleep/wake sessions in subjects whose sleep patterns are
disrupted by respiratory infection (H3N2 flu virus). Pre-symptomatic features
based on the detected periods are found to be strongly predictive of both
infection status (AUC = 0.844) and infection onset time (AUC = 0.885),
indicating the effectiveness and usefulness of the algorithm. Conclusion: Our
method can effectively detect sleep/wake states in the presence of sleep
pattern shift. Significance: Utilizing integrated multisensor signal processing
and adaptive training schemes, our algorithm is able to capture key sleep
patterns in ambulatory monitoring, leading to better automated sleep assessment
and prediction.
