  Sensor technologies are becoming increasingly prevalent in the biomedical
field, with applications ranging from telemonitoring of people at risk, to
using sensor derived information as objective endpoints in clinical trials. To
fully utilize sensor information, signals from distinct sensors often have to
be temporally aligned. However, due to imperfect oscillators and significant
noise, commonly encountered with biomedical signals, temporal alignment of raw
signals is an all but trivial problem, with, to-date, no generally applicable
solution. In this work, we present Deep Canonical Correlation Alignment (DCCA),
a novel, generally applicable solution for the temporal alignment of raw
(biomedical) sensor signals. DCCA allows practitioners to directly align raw
signals, from distinct sensors, without requiring deep domain knowledge. On a
selection of artificial and real datasets, we demonstrate the performance and
utility of DCCA under a variety of conditions. We compare the DCCA algorithm to
other warping based methods, DCCA outperforms dynamic time warping and cross
correlation based methods by an order of magnitude in terms of alignment error.
DCCA performs especially well on almost periodic biomedical signals such as
heart-beats and breathing patterns. In comparison to existing approaches, that
are not tailored towards raw sensor data, DCCA is not only fast enough to work
on signals with billions of data points but also provides automatic filtering
and transformation functionalities, allowing it to deal with very noisy and
even morphologically distinct signals.
