Abstract

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.

Authors

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

Keywords

ECG, QRS-Complex, Arrhythmia Classification, LSTM, Signal Processing