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@PHDTHESIS{FarhadiGhalati:1026205,
author = {Farhadi Ghalati, Pejman},
othercontributors = {Schuppert, Andreas and Steinseifer, Ulrich},
title = {{E}nhanced automatic feature extraction in heterogeneous
biomedical time series for optimization of clinical
monitoring and diagnosis},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2026-00736},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {The prevalence of real-time data acquisition has increased
significantly in recent years due to advancements in
sensors, transmission, and storage technologies. This has
led to the collection of large datasets in various fields,
including engineering, medical, physical, and social
sciences. In healthcare, dynamic monitoring data has become
a crucial aspect for health diagnosis, treatment, and
proactive health tracking. The emergence of wearable
technology and monitoring systems has provided new avenues
for understanding complex dynamic processes through
longitudinal data, providing insight into an individual's
health and comfort. However, it is important to note that
data quality issues can significantly impact temporal data
analysis. Additionally, the analysis strategies required for
monitoring data vary greatly between monitoring a few
parameters at high sampling rates and monitoring several
parameters over extended periods at low sampling
frequencies. One approach for improving such data analysis
is to represent the data as features, enhancing the
interoperability of outcomes from statistical and machine
learning analyses. This thesis explores the domain knowledge
feature engineering of monitoring time series data through a
Python package, developed to provide a robust and systematic
temporal data analysis platform. The package includes
functional elements ranging from data reading to denoising
and feature extraction, and its modular design allows for
diverse applications and the incorporation of user-defined
components. The findings of this thesis demonstrate the
capability of temporal data abstraction using a methodical
feature extraction scheme. The developed workflow provides a
robust and user-friendly architecture for conducting time
series analysis.},
cin = {530000-4 ; 925310 / $400000_20140620$},
ddc = {620},
cid = {$I:(DE-82)530000-4_20190813$ / $I:(DE-82)400000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2026-00736},
url = {https://publications.rwth-aachen.de/record/1026205},
}
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