7. Automatic analysis of cardiovascular diseases using EMD and support vector machines
-
Sweta Kumari
Abstract
There has been a significant growth in the global mortalities due to cardiac diseases. Electrocardiography (ECG) is a low-cost, efficient, and noninvasive tool widely used to study the cardiac status. It behaves as a non-linear and non-stationary characteristic due to which it is difficult to analyze them with a naked eye. Therefore, the cardiac healthcare demands an automated tool to analyze long-term heartbeat records. In literature, a vast majority of methodologies are reported to perform analysis on the ECG, however, they fail to provide a complete solution to this challenge. This chapter employs Hilbert-Huang transform (HHT) for feature selection, which is demonstrated as an effective technique as such it is capable to provide frequency spectrum varying with time. As a result, the output coefficients are used to extract different features such as weighted mean frequency, skewness, central moment, and many more processed from the intrinsic mode functions extricated utilizing the empirical mode decomposition (EMD) calculation. These features extracted are applied to support vector machine (SVM) model for identification into subsequent classes of ECG signals. The validation of proposed methodology is performed on the Physionet data to identify six categories of heartbeats. The methodology reported a higher accuracy of 99.18% in comparison with previous methodologies reported. The methodology can be utilized as a solution for computerized diagnosis of cardiac diseases to serve the cardiac healthcare.
Abstract
There has been a significant growth in the global mortalities due to cardiac diseases. Electrocardiography (ECG) is a low-cost, efficient, and noninvasive tool widely used to study the cardiac status. It behaves as a non-linear and non-stationary characteristic due to which it is difficult to analyze them with a naked eye. Therefore, the cardiac healthcare demands an automated tool to analyze long-term heartbeat records. In literature, a vast majority of methodologies are reported to perform analysis on the ECG, however, they fail to provide a complete solution to this challenge. This chapter employs Hilbert-Huang transform (HHT) for feature selection, which is demonstrated as an effective technique as such it is capable to provide frequency spectrum varying with time. As a result, the output coefficients are used to extract different features such as weighted mean frequency, skewness, central moment, and many more processed from the intrinsic mode functions extricated utilizing the empirical mode decomposition (EMD) calculation. These features extracted are applied to support vector machine (SVM) model for identification into subsequent classes of ECG signals. The validation of proposed methodology is performed on the Physionet data to identify six categories of heartbeats. The methodology reported a higher accuracy of 99.18% in comparison with previous methodologies reported. The methodology can be utilized as a solution for computerized diagnosis of cardiac diseases to serve the cardiac healthcare.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
