Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III
This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a pa...
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2018
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| Online Access: | http://eprints.utm.my/81819/1/HaniMKhalil2018_ParoxysmalAtrialFibrillationPredictionBasedonHRV.pdf |
| _version_ | 1796863463541178368 |
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| author | Boon, K. H. Khalil Hani, M. Malarvili, M. B. |
| author_facet | Boon, K. H. Khalil Hani, M. Malarvili, M. B. |
| author_sort | Boon, K. H. |
| collection | ePrints |
| description | This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. |
| first_indexed | 2024-03-05T20:27:08Z |
| format | Article |
| id | utm.eprints-81819 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:27:08Z |
| publishDate | 2018 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-818192019-09-29T08:13:22Z http://eprints.utm.my/81819/ Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III Boon, K. H. Khalil Hani, M. Malarvili, M. B. TK Electrical engineering. Electronics Nuclear engineering This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Elsevier B.V. 2018-01 Article PeerReviewed application/pdf en http://eprints.utm.my/81819/1/HaniMKhalil2018_ParoxysmalAtrialFibrillationPredictionBasedonHRV.pdf Boon, K. H. and Khalil Hani, M. and Malarvili, M. B. (2018) Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Computer Methods and Programs in Biomedicine, 153 . pp. 171-184. ISSN 0169-2607 http://dx.doi.org/10.1016/j.cmpb.2017.10.012 DOI:10.1016/j.cmpb.2017.10.012 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Boon, K. H. Khalil Hani, M. Malarvili, M. B. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title | Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title_full | Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title_fullStr | Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title_full_unstemmed | Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title_short | Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III |
| title_sort | paroxysmal atrial fibrillation prediction based on hrv analysis and non dominated sorting genetic algorithm iii |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/81819/1/HaniMKhalil2018_ParoxysmalAtrialFibrillationPredictionBasedonHRV.pdf |
| work_keys_str_mv | AT boonkh paroxysmalatrialfibrillationpredictionbasedonhrvanalysisandnondominatedsortinggeneticalgorithmiii AT khalilhanim paroxysmalatrialfibrillationpredictionbasedonhrvanalysisandnondominatedsortinggeneticalgorithmiii AT malarvilimb paroxysmalatrialfibrillationpredictionbasedonhrvanalysisandnondominatedsortinggeneticalgorithmiii |