Auto-FS-Cardiac: Optimizing ECG Heartbeat Classification with Automated Feature Selection using TPOT Template Framework
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Othman:2023:AIHCIR,
-
author = "Hiba A. Othman and Qi Zhao and Lijiang Chen and
Zhibo Hong and Yu Chen2",
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title = "Auto-{FS-Cardiac:} Optimizing {ECG} Heartbeat
Classification with Automated Feature Selection using
{TPOT} Template Framework",
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booktitle = "2023 2nd International Conference on Artificial
Intelligence, Human-Computer Interaction and Robotics
(AIHCIR)",
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year = "2023",
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pages = "179--185",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Human
computer interaction, Adaptation models, Heart beat,
Pipelines, Machine learning, Electrocardiography,
AutoML, TPOT, ECG, Ar-rhythmia, Feature set selector,
TPOT-Template - Genetics Programming Introduction",
-
DOI = "
doi:10.1109/AIHCIR61661.2023.00037",
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abstract = "Cardiovascular diseases (CVDs) constitute a
significant global health concern with a profound
impact on mortality rates. Recent advancements in
artificial intelligence (AI) have facilitated the
successful application of automated classification
methods for cardiac arrhythmias. This paper introduces
'Auto-FS-Cardiac,' an innovative automated feature
selection model. Leveraging Automated Machine Learning
(Au-toML) and the Tree-based Pipeline Optimisation Tool
(TPOT) framework, the model constructs a classification
pipeline aimed at distinguishing between five distinct
heartbeats in electro-cardiogram (ECG) data sourced
from the MIT-BIH database. The study evaluates the
performance of Auto-FS-Cardiac under both automated and
predefined human-expert feature selection scenarios.
Additionally, a comparative analysis with traditional
feature selection models provides insights into the
proficiency of Auto-FS-Cardiac in generating optimal
pipelines for precise ECG heartbeat classification.
Auto-FS-Cardiac performance, achieved an accuracy level
of 0.9569 with a rapid execution time of 1.9857
seconds. Notably, when using predefined features, the
model maintains a consistent accuracy score of 0.9522,
albeit with a longer execution time of 14.7836 seconds.
This highlights the model's adaptability in balancing
high accuracy and efficiency when autonomously managing
the feature selection process. The observed tradeoff
between efficiency and interpretability suggests that
interventions in feature selection may impact these
factors.",
-
notes = "Also known as \cite{10505432}
Beihang University, China, Beijing, China",
- }
Genetic Programming entries for
Hiba A Othman
Qi Zhao
Lijiang Chen
Zhibo Hong
Yu Chen2
Citations