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📄 Abstract
Abstract: Early detection of heart arrhythmia can prevent severe future complications
in cardiac patients. While manual diagnosis still remains the clinical
standard, it relies heavily on visual interpretation and is inherently
subjective. In recent years, deep learning has emerged as a powerful tool to
automate arrhythmia detection, offering improved accuracy, consistency, and
efficiency. Several variants of convolutional and recurrent neural network
architectures have been widely explored to capture spatial and temporal
patterns in physiological signals. However, despite these advancements, current
models often struggle to generalize well in real-world scenarios, especially
when dealing with small or noisy datasets, which are common challenges in
biomedical applications. In this paper, a novel CNN-H-Infinity-LSTM
architecture is proposed to identify arrhythmic heart signals from heart sound
recordings. This architecture introduces trainable parameters inspired by the
H-Infinity filter from control theory, enhancing robustness and generalization.
Extensive experimentation on the PhysioNet CinC Challenge 2016 dataset, a
public benchmark of heart audio recordings, demonstrates that the proposed
model achieves stable convergence and outperforms existing benchmarks, with a
test accuracy of 99.42% and an F1 score of 98.85%.
Key Contributions
Proposes a novel CNN-H-Infinity-LSTM architecture for detecting cardiac arrhythmias from heart sound recordings. This architecture integrates trainable parameters inspired by the H-Infinity filter to enhance robustness and generalization, particularly in the presence of noisy or small datasets.
Business Value
Enables earlier and more accurate detection of heart arrhythmias, potentially preventing severe complications and reducing healthcare costs associated with misdiagnosis or delayed treatment.