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Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia
Toktam Khatibi1,2, Nooshin Rabinezhadsadatmahaleh3
1Faculty of Industrial and Systems Engineering, Tarbiat Modares University (TMU), 14117-13114, Tehran, Iran. toktamk.khatibi@modares.ac.ir.
Insights
This study introduces a novel deep learning and K-NNs feature engineering method for accurate arrhythmia detection from electrocardiogram (ECG) beats. The proposed approach achieves high accuracy, offering a valuable tool for automated heartbeat classification.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Arrhythmia, characterized by abnormal heart rhythms, poses diagnostic challenges due to ECG variability.
- Manual electrocardiogram (ECG) interpretation is prone to errors and requires specialized expertise.
- Computer-aided diagnosis systems are crucial for accessible arrhythmia detection, especially in remote areas.
Purpose of the Study:
- To develop and evaluate a novel feature engineering method for classifying ECG beats for arrhythmia detection.
- To classify four distinct heartbeat classes using the proposed methodology.
- To compare the performance of the proposed method against traditional machine learning and deep learning approaches.
Main Methods:
- A novel feature engineering technique combining deep learning and K-Nearest Neighbors (K-NNs) was developed.
- Extracted features were classified using various algorithms including decision trees, Support Vector Machines (SVMs), and random forests.
- A fivefold cross-validation strategy was employed to assess performance.
Main Results:
- The proposed method achieved high classification performance with an average Accuracy of 99.77%, AUC of 99.99%, Precision of 99.75%, and Recall of 99.30%.
- Demonstrated a favorable balance between sensitivity and specificity, indicating robust feature extraction capabilities.
- Exhibited significantly lower computational time compared to training deep learning models from scratch.
Conclusions:
- The developed feature engineering method provides a highly accurate and computationally efficient solution for ECG beat classification and arrhythmia detection.
- This approach offers a promising alternative to traditional machine learning and full deep learning models for automated cardiac rhythm analysis.
- The method's effectiveness supports its suitability for developing accessible computer-aided diagnosis tools for heart rhythm disorders.
Abstract:
Arrhythmia is slow, fast or irregular heartbeat. Manual ECG assessment and disease classification is an error-prone task because of vast differences in ECG morphology and difficulty in accurate identifying ECG components. Moreover, proposing a computer-aided diagnosis system for heartbeat classification can be useful when access to medical care centers is difficult or impossible. Therefore, the main aim of this study is classifying ECG beats for arrhythmia detection (four beat classes are considered). Previous studies have proposed different methods based on traditional machine learning and/or deep learning. In this paper, a novel feature engineering method is proposed based on deep learning and K-NNs. The features extracted by our proposed method are classified with different classifiers such as decision trees, SVMs with different kernels and random forests. Our proposed method has reasonably good performance for beat classification and achieves the average Accuracy of 99.77%, AUC of 99.99%, Precision of 99.75% and Recall of 99.30% using fivefold Cross Validation strategy. The main advantage of the proposed method is its low computational time compared to training deep learning models from scratch and its high accuracy compared to the traditional machine learning models. The strength and suitability of the proposed method for feature extraction is shown by the high balance between sensitivity and specificity.
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