International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 06 | Jun 2025 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
ELECTROCARDIOGRAM HEARTBEAT CLASSIFICATION USING MACHINE LEARNING AND ENSEMBLE CONVOLUTIONAL NEURAL NETWORKBIDIRECTIONAL LONG SHORT-TERM MEMORY TECHNIQUE NIRMALA DEVI N1 and RATHI S2 1PG Scholar, Dept. of CSE, Government College of Technology, Coimbatore, India 2 Professor, Dept. of CSE, Government College of Technology, Coimbatore, India
------------------------------------------------------------------------***-------------------------------------------------------------------arrhythmias can impair circulation and potentially harm Abstract - This project aims to improve the automated
vital organs, including the brain and heart. However, manual examination of ECGs is labor-intensive, prone to errors, and unsuitable for processing large-scale data in clinical settings. Additional challenges, including noise in signals, imbalanced data, and the temporal nature of ECG signals, complicate automated analysis. These limitations emphasize the need for advanced, efficient systems for classifying heartbeats to support early arrhythmia diagnosis.
classification of electrocardiograms (ECGs) for the early detection of cardiac arrhythmias, which is essential for diagnosing cardiovascular diseases (CVDs). It tackles significant challenges such as time-series dependencies in ECG signals and class imbalance. The proposed approach uses the Random Forest method, an ensemble learning technique, for classifying ECG heartbeats. The model was trained using the MIT-BIH Arrhythmia Database, which consists of over 109,000 labeled heartbeats, and incorporates SMOTE to address class imbalance. Advanced preprocessing methods, including median and SavitzkyGolay filters, are applied to enhance the reliability of the dataset. Achieving an impressive correctness of 99.88%, this Random Forest model surpasses previous methods in detecting complex arrhythmias such as ventricular and supraventricular beats. These findings demonstrate the model’s potential for telemedicine applications and its ability to provide reliable CVD diagnoses. Future research should focus on utilizing larger datasets and improving feature extraction methods to further optimize model performance.
To address these challenges, the paper introduces an innovative methodology combining machine learning ensemble techniques with A combined deep learning framework that leverages CNN architectures and Bidirectional LSTM networks. This approach effectively addresses issues such as class imbalance and temporal dependencies in ECG data while enhancing classification accuracy. Key aspects of the methodology include: 1.Dataset: The study uses the MIT-BIH Arrhythmias Database, a comprehensive resource containing over 109,000 ECG beats categorized into Normal, Supraventricular, Ventricular, Fusion, and Unknown classes.
Keywords— Electrocardiogram, Cardiac Arrhythmia Detection, Deep learning techniques such as Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, Machine Learning, Ensemble Learning, Random Forest, Support Vector Machine, XGBoost, Adaboost, Stacked Ensemble, MIT-BIH Arrhythmia Database.
2.Preprocessing: Advanced noise reduction techniques, including median and Savitzky-Golay filters, are applied. The signals are segmented into heartbeat intervals using established algorithms like Pan-Tompkins.
1.INTRODUCTION
3.ClassImbalance: The Synthetic Minority Oversampling Technique is utilized to address data imbalance, particularly for underrepresented arrhythmia classes.
The paper highlights the significant impact of cardiovascular diseases as the primary cause of death worldwide, using a considerable proportion among deaths attributed to heart attacks. Detecting cardiac arrhythmias early is essential for effective disease management and improving patient outcomes. Electrocardiograms, as nonInternal and affordable diagnostic tools, are commonly used to identify irregularities in heart rhythms. A standard ECG includes elements like the P-wave, QRS complex, and T-wave, and occasionally the U-wave, with deviations from these patterns often indicating arrhythmias. Undiagnosed
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4.Feature Extraction: Features are extracted using a timeseries library, TSFEL, and optimized through recursive feature elimination (RFE) to focus on the most relevant attributes. 5.Classification: The proposed framework combines ensemble machine learning methods, including Random Forest and Support Vector Machines, XGBoost, as well as Adaboost, with a CNN-BiLSTM hybrid model. CNN captures
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