Automated diagnosis of cardiac health using recurrence quantification analysis
The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in th...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/96466 http://hdl.handle.net/10220/11404 |
| _version_ | 1826124328119304192 |
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| author | Ng, Kwan-Hoong Swapna, Goutham Krishnan, M. Muthu Rama Sree, Subbhuraam Vinitha Ng, Eddie Yin-Kwee Ghista, Dhanjoo N. Ang, Alvin P. C. Suri, Jasjit S. |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Ng, Kwan-Hoong Swapna, Goutham Krishnan, M. Muthu Rama Sree, Subbhuraam Vinitha Ng, Eddie Yin-Kwee Ghista, Dhanjoo N. Ang, Alvin P. C. Suri, Jasjit S. |
| author_sort | Ng, Kwan-Hoong |
| collection | NTU |
| description | The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets. |
| first_indexed | 2024-10-01T06:18:50Z |
| format | Journal Article |
| id | ntu-10356/96466 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:18:50Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/964662020-03-07T13:22:17Z Automated diagnosis of cardiac health using recurrence quantification analysis Ng, Kwan-Hoong Swapna, Goutham Krishnan, M. Muthu Rama Sree, Subbhuraam Vinitha Ng, Eddie Yin-Kwee Ghista, Dhanjoo N. Ang, Alvin P. C. Suri, Jasjit S. School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Bio-mechatronics The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets. 2013-07-15T04:35:39Z 2019-12-06T19:31:10Z 2013-07-15T04:35:39Z 2019-12-06T19:31:10Z 2012 2012 Journal Article Krishnan, M. M. R., Sree, S. V., Ghista, D. N., Ng, E. Y. K., Swapna, Ang, A. P. C., et al. (2012). Automated diagnosis of cardiac health using recurrence quantification analysis. Journal of mechanics in medicine and biology, 12(04), 1240014-. https://hdl.handle.net/10356/96466 http://hdl.handle.net/10220/11404 10.1142/S0219519412400143 en Journal of mechanics in medicine and biology © 2012 World Scientific Publishing Company |
| spellingShingle | DRNTU::Engineering::Mechanical engineering::Bio-mechatronics Ng, Kwan-Hoong Swapna, Goutham Krishnan, M. Muthu Rama Sree, Subbhuraam Vinitha Ng, Eddie Yin-Kwee Ghista, Dhanjoo N. Ang, Alvin P. C. Suri, Jasjit S. Automated diagnosis of cardiac health using recurrence quantification analysis |
| title | Automated diagnosis of cardiac health using recurrence quantification analysis |
| title_full | Automated diagnosis of cardiac health using recurrence quantification analysis |
| title_fullStr | Automated diagnosis of cardiac health using recurrence quantification analysis |
| title_full_unstemmed | Automated diagnosis of cardiac health using recurrence quantification analysis |
| title_short | Automated diagnosis of cardiac health using recurrence quantification analysis |
| title_sort | automated diagnosis of cardiac health using recurrence quantification analysis |
| topic | DRNTU::Engineering::Mechanical engineering::Bio-mechatronics |
| url | https://hdl.handle.net/10356/96466 http://hdl.handle.net/10220/11404 |
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