  Fluctuations in heart rate are intimately tied to changes in the
physiological state of the organism. We examine and exploit this relationship
by classifying a human subject's wake/sleep status using his instantaneous
heart rate (IHR) series. We use a convolutional neural network (CNN) to build
features from the IHR series extracted from a whole-night electrocardiogram
(ECG) and predict every 30 seconds whether the subject is awake or asleep. Our
training database consists of 56 normal subjects, and we consider three
different databases for validation; one is private, and two are public with
different races and apnea severities. On our private database of 27 subjects,
our accuracy, sensitivity, specificity, and AUC values for predicting the wake
stage are 83.1%, 52.4%, 89.4%, and 0.83, respectively. Validation performance
is similar on our two public databases. When we use the photoplethysmography
instead of the ECG to obtain the IHR series, the performance is also
comparable. A robustness check is carried out to confirm the obtained
performance statistics. This result advocates for an effective and scalable
method for recognizing changes in physiological state using non-invasive heart
rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as
its location in time and is suitable for differentiating between the wake and
sleep stages.
