Advanced signal processing techniques are critical in the early
detection and classification of cardiac abnormalities. This study
addresses the challenge of detecting QRS-complexes and classifying
arrhythmias in embedded systems. Traditional methods often struggle
with high false detection rates and computational inefficiencies. Our
approach leverages Long Short-Term Memory (LSTM) networks to
enhance detection accuracy and classification performance by
integrating hybridized features from electrocardiogram (ECG) signals.
We propose a novel framework that combines time-domain features
with frequency-domain characteristics, optimizing signal
preprocessing and feature extraction. The LSTM model was trained on
a dataset of 10,000 ECG records, achieving a QRS detection accuracy
of 98.5% and an arrhythmia classification accuracy of 95.3%. Our
embedded system implementation demonstrates real-time processing
capabilities with a latency of 32 milliseconds per signal. The results
indicate substantial improvements in both detection precision and
classification reliability, making our system a robust solution for
embedded cardiac monitoring applications.
T. Thulasimani1, K. VenkataRamana2, D. Sudhakar3, T. Kalai Selvi4, D. Satheesh Kumar5
Bannari Amman Institute of Technology, India1, Sri Vasavi Engineering College, India2, Annamacharya PG College of Computer Studies, India3, Erode Sengunthar Engineering College, India4, Hindusthan College of Engineering and Technology, India5
ECG, QRS-Complex, Arrhythmia Classification, LSTM, Signal Processing
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| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 10 , Issue: 2 , Pages: 1795 - 1799 )
Date of Publication :
July 2024
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91
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