![](bg8.png)
Another line of research that will produce relevant
insights is the development of loss concealment tech-
niques to efficiently repair the ECG stream at the re-
ceiver, with or without the cooperation of the sender.
We specifically envision the use of linear prediction,
Kalman filters or other adaptive filters to reconstruct
the ECG signal. Although this may not provide a
strong improvement over simpler techniques for the
purpose of heart beat detection, it shall play a sig-
nificant role in the performance of subsequent, more
ellaborate algorithms, like arrhythmia detection.
Taking a holistic view of the problem, we fur-
ther propose the development of network-aware sig-
nal processing algorithms that are either resilient or
can adapt to certain levels of sample loss. We envision
the application of non-uniform sampling mechanisms
and results from the field of compressed sensing.
10 CONCLUSIONS
We address the often ignored problem of transmis-
sion of biomedical signal data across networks for re-
mote processing, proposing a framework that models
the relevant building blocks of such a system. We
use the framework to perform an initial numerical
evaluation of the impact of uncorrelated random net-
work packet losses in the performance of the well-
known heart beat detection algorithm ecgpuwave us-
ing the MIT-BIH database. Our results show that
1) packet losses cause significant degradation of the
heart beat detection algorithms; 2) simple loss con-
cealment techniques, like insertion of last known sam-
ple and linear interpolation, significantly reduce the
impact of network losses, but their performance de-
pends on the packetization used; 3) packetization con-
stitutes an important parameter to choose the trade-
off amongst network and energy efficiency and im-
pact of packet losses; 5) there is not one combination
of packetization and loss concealment technique that
performs best for all network scenarios studied.
As a consequence of these findings, we identify
the need to further research the transmission of data
from biomedical signals across networks and propose
to deepen the understanding of the applicability of
three fields of research to biomedical signal trans-
mission and processing. Namely, 1) the joint opti-
mization of transmission parameters, 2) the develop-
ment of advanced loss concealment techniques, like
Kalman filters and linear prediction, and 3) the devel-
opment of loss-resilient signal processing algorithms,
leveraging results from compressed sensing or non-
uniform sampling theory.
ACKNOWLEDGEMENTS
The authors thank Miguel Coimbra and Can Ye for
fruitful discussions.
This work was supported by FCT (Fundac¸
˜
ao
para a Ci
ˆ
encia e a Tecnologia) through the
VR (Vital Responder) project (within the
CarnegieMellon—Portugal program. ref. CMU-
P/CPS/0046/2008).
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