  Manic episodes of bipolar disorder can lead to uncritical behaviour and
delusional psychosis, often with destructive consequences for those affected
and their surroundings. Early detection and intervention of a manic episode are
crucial to prevent escalation, hospital admission and premature death. However,
people with bipolar disorder may not recognize that they are experiencing a
manic episode and symptoms such as euphoria and increased productivity can also
deter affected individuals from seeking help. This work proposes to perform
user-independent, automatic mood-state detection based on actigraphy and
electrodermal activity acquired from a wrist-worn device during mania and after
recovery (euthymia). This paper proposes a new deep learning-based ensemble
method leveraging long (20h) and short (5 minutes) time-intervals to
discriminate between the mood-states. When tested on 47 bipolar patients, the
proposed classification scheme achieves an average accuracy of 91.59% in
euthymic/manic mood-state recognition.
