Cardiac Arrhythmia Detection by ECG Feature Extraction: A Machine Learning Based Approach
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Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
ECG beats are vital for reducing fatalities from CVDs by enabling arrhythmia
detection through intelligent systems, which provide crucial cardiac insights to
specialists. However, challenges such as noise, heartbeat instability, and
imbalance affect the accuracy and speed of these systems. Accurate diagnosis
in quick time is essential for proper treatment and patient recovery. This study
focuses on enhancing the precise diagnosis of various CVD types by analyzing
arrhythmias in ECG signals of the heartbeats. We developed a deep learning
based arrhythmia detection system that utilizes discrete wavelet transformation
during pre-processing of the signals and the SMOTE oversampling algorithm to
deal with the class imbalance problem. Our classifier integrates a Convolutional
Neural Network (CNN) for spatial pattern detection with a Bidirectional Long
Short-Term Memory (BLSTM) network for temporal dependency identification.
We trained and evaluated our system using the MIT-BIH Arrhythmia Dataset.The
evaluation results demonstrate that our method, after 50 training epochs,
achieves high accuracy in different categories: 99.65% for class F, 99.37% for
class V, 98.45% for class N, 99.10% for class S, and 99.82% for class Q. This
proposed deep learning based system can be employed for the automatic
diagnosis of arrhythmia and assist the CVD specialists in accurate diagnosis.
Description
Supervised by
Ms. Sanjida Ali,
Lecturer,
Department of Electrical and Electronic Engineering (EEE)
Islamic University of Technology (IUT)
Board Bazar, Gazipur, Bangladesh
This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024
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