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ECG Beats Classification Using Mixture of Features
1Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Orissa 769008, India.
Insights
This study introduces an efficient system for classifying electrocardiogram (ECG) signals into five types of heartbeats. The proposed method enhances accuracy in diagnosing heart conditions by effectively analyzing ECG data.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Cardiology
Background:
- Accurate classification of electrocardiogram (ECG) signals is crucial for diagnosing heart disease.
- Existing methods for ECG beat classification face challenges in distinguishing between various arrhythmias.
Purpose of the Study:
- To design and evaluate an efficient system for classifying five types of ECG beats: normal (N), ventricular ectopic (V), supraventricular ectopic (S), fusion (F), and unknown (Q).
- To compare the performance of novel feature extraction techniques with existing methods for ECG beat classification.
Main Methods:
- Proposed two feature extraction methods: S-transform based features with temporal features, and a combination of S-transform (ST) and Wavelet Transform (WT) based features with temporal features.
- Utilized a multilayer perceptron neural network (MLPNN) classifier for independent classification of the extracted features.
- Evaluated performance on the MIT-BIH arrhythmia database, comparing three feature extraction techniques against AAMI standards for five ECG beat classes.
Main Results:
- The proposed feature extraction techniques demonstrated superior performance compared to existing methods.
- Achieved average sensitivity of 95.70% for Normal (N), 78.05% for Supraventricular ectopic (S), 49.60% for Fusion (F), 89.68% for Ventricular ectopic (V), and 33.89% for Unknown (Q) beats.
Conclusions:
- The developed system offers an efficient and effective approach for ECG beat classification.
- The proposed feature extraction techniques show significant potential for improving the accuracy of automated cardiac arrhythmia diagnosis.
Abstract:
Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.
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