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Cardiac arrhythmia beat classification using DOST and PSO tuned SVM
Sandeep Raj1, Kailash Chandra Ray1, Om Shankar2
1Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta, Patna 801103, India.
Insights
This study introduces an automated method for detecting cardiac arrhythmias using electrocardiogram (ECG) signals. The approach enhances classification accuracy for computer-aided diagnosis, improving upon existing methods.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Cardiovascular diseases (CVDs) are a leading cause of death, necessitating accurate and efficient diagnostic tools.
- Electrocardiogram (ECG) signal analysis is crucial for diagnosing CVDs, but manual interpretation of long-term recordings is time-consuming and challenging.
- Existing signal processing techniques for ECG analysis face limitations due to the non-stationary nature of these signals.
Purpose of the Study:
- To develop an automated diagnostic solution for cardiac arrhythmia detection.
- To improve the classification accuracy rate of ECG signal analysis.
- To address the limitations of current methods in handling non-stationary ECG data.
Main Methods:
- A four-stage methodology involving filtering, R-peak detection, feature extraction, and classification.
- Wavelet-based filtering and the Pan-Tompkins algorithm for R-peak detection.
- Discrete Orthogonal Stockwell Transform (DOST) for time-frequency feature extraction, combined with Principal Component Analysis (PCA) and dynamic features, classified using Particle Swarm Optimization (PSO)-tuned Support Vector Machines (SVM).
Main Results:
- The proposed method achieved a 99.18% accuracy for 16 classes in a category-based assessment and 89.10% accuracy for 5 classes in a patient-based assessment on the MIT-BIH arrhythmia database.
- These results demonstrate improved performance compared to state-of-the-art diagnostic methods.
- The methodology was validated on the benchmark MIT-BIH arrhythmia database.
Conclusions:
- The novel feature representation and PSO-optimized SVM classifier significantly enhance classification accuracy for cardiac arrhythmias.
- The developed system offers a promising automated computer-aided diagnosis (CAD) solution for cardiac arrhythmia beats.
- The approach effectively handles the non-stationary characteristics of ECG signals.
Background And Objective:
The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias.
Methods:
The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM).
Results:
The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature.
Conclusions:
The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats.
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