Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy
Abstract Background This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF sign...
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| Format: | Article |
| Language: | English |
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BMC
2017-10-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12938-017-0406-z |
| _version_ | 1818908556753108992 |
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| author | Yi Xin Yizhang Zhao |
| author_facet | Yi Xin Yizhang Zhao |
| author_sort | Yi Xin |
| collection | DOAJ |
| description | Abstract Background This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction. Results The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively. Conclusions Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment. |
| first_indexed | 2024-12-19T22:12:54Z |
| format | Article |
| id | doaj.art-8e13af5db33e41c6a4b8523474ba70c3 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-19T22:12:54Z |
| publishDate | 2017-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-8e13af5db33e41c6a4b8523474ba70c32022-12-21T20:03:51ZengBMCBioMedical Engineering OnLine1475-925X2017-10-0116111210.1186/s12938-017-0406-zParoxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropyYi Xin0Yizhang Zhao1Department of Biomedical Engineering, Beijing Institute of TechnologyDepartment of Biomedical Engineering, Beijing Institute of TechnologyAbstract Background This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction. Results The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively. Conclusions Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.http://link.springer.com/article/10.1186/s12938-017-0406-zPAFHRV analysisMulti-scale wavelet entropySVM |
| spellingShingle | Yi Xin Yizhang Zhao Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy BioMedical Engineering OnLine PAF HRV analysis Multi-scale wavelet entropy SVM |
| title | Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy |
| title_full | Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy |
| title_fullStr | Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy |
| title_full_unstemmed | Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy |
| title_short | Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy |
| title_sort | paroxysmal atrial fibrillation recognition based on multi scale wavelet α entropy |
| topic | PAF HRV analysis Multi-scale wavelet entropy SVM |
| url | http://link.springer.com/article/10.1186/s12938-017-0406-z |
| work_keys_str_mv | AT yixin paroxysmalatrialfibrillationrecognitionbasedonmultiscalewaveletaentropy AT yizhangzhao paroxysmalatrialfibrillationrecognitionbasedonmultiscalewaveletaentropy |