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@PHDTHESIS{Loh:1013853,
author = {Loh, Johnson Luo},
othercontributors = {Gemmeke, Tobias and Schmeink, Anke},
title = {{N}euro-inspired hardware acceleration for efficient
biomedical signal processing in mobile sensing devices},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-05740},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {The processing of biomedical signals in mobile sensing
devices enables the continuous monitoring of health
parameters for early detection of threatening arrhythmia in
the population through convenient wearable devices, such as
smartwatches. The design of processing modules, which are
feasible in this resource-constrained environment, is
subject to multiple constraints for their deployment
in-field. High quality classification is desired for
accurate detection to trigger treatment by trained
personnel. Robust classification beyond available training
data is necessary to generalize system feasibility across
the general population. Low-power operation is necessary for
long-termscreening for sparse features indicating abnormal
health conditions. The co-optimization of neuro-inspired
algorithms on dedicated hardware shows the promise to
address all desired specifications in an
application-specific device. This work explores
neuro-inspired concepts for low-power digital processing of
biomedical signals. Artificial neural networks has shown
superior classification capabilities in the machine learning
domain. Using an artifical neural network as a baseline, a
systematic design space exploration methodology is applied
to design an ECG classifier and co-optimize the system from
the algorithm level down to a hardware design for ultra-low
power consumption and high classification quality. Then, the
system is extended with a domain generalization method for
robust classification across multiple datasets. The method
is designed for direct integration into a pre-trained neural
network with low overhead regarding inference and training.
At last, the temporal coding of information in spikes is
adopted from the human brain as a data processing mechanism
for low power processing. The investigated temporal coding
method shows equivalent numerical values after processing
with reduced operations compared to conventional fixed-point
arithmetic. In the end, the neuro-inspired concepts show
promising directions to improve specialized ANN hardware
accelerators for biomedical signal processing both for
low-power processing and robust high-quality
classification.},
cin = {611110},
ddc = {621.3},
cid = {$I:(DE-82)611110_20170101$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-05740},
url = {https://publications.rwth-aachen.de/record/1013853},
}
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