Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis
A method to detect the locations of premature ventricular contraction (PVC) and reconstruct electrocardiogram (ECG) is proposed in this work. The ECG reconstruction is based on the linear prediction method. For the sake of known ground truths, a set of PVC beats was synthetically inserted to ECG fro...
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2014
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th-cmuir.6653943832-16412014-08-29T09:29:33Z Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis Theera-Umpon N. Phiphatkhunarnon P. Auephanwiriyakul S. A method to detect the locations of premature ventricular contraction (PVC) and reconstruct electrocardiogram (ECG) is proposed in this work. The ECG reconstruction is based on the linear prediction method. For the sake of known ground truths, a set of PVC beats was synthetically inserted to ECG from the MIT-BIH normal sinus rhythm database. The experimental results show that the heart rate variability (HRV) measures derived from the reconstructed ECG's are very close to the ground truths in both time domain and frequency domain. The comparison to the methods commonly used by experts, i.e., removing the abnormal RR intervals or averaging RR intervals before calculating the HRV measures, also shows the better performance by the proposed method. © 2013 Springer Science+Business Media Dordrecht. 2014-08-29T09:29:33Z 2014-08-29T09:29:33Z 2013 Conference Paper 9789400765153 18761100 10.1007/978-94-007-6516-0_30 98013 http://www.scopus.com/inward/record.url?eid=2-s2.0-84880729696&partnerID=40&md5=fb4d39f2d37055f2af17f9db3fc92d35 http://cmuir.cmu.ac.th/handle/6653943832/1641 English |
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A method to detect the locations of premature ventricular contraction (PVC) and reconstruct electrocardiogram (ECG) is proposed in this work. The ECG reconstruction is based on the linear prediction method. For the sake of known ground truths, a set of PVC beats was synthetically inserted to ECG from the MIT-BIH normal sinus rhythm database. The experimental results show that the heart rate variability (HRV) measures derived from the reconstructed ECG's are very close to the ground truths in both time domain and frequency domain. The comparison to the methods commonly used by experts, i.e., removing the abnormal RR intervals or averaging RR intervals before calculating the HRV measures, also shows the better performance by the proposed method. © 2013 Springer Science+Business Media Dordrecht. |
format |
Conference or Workshop Item |
author |
Theera-Umpon N. Phiphatkhunarnon P. Auephanwiriyakul S. |
spellingShingle |
Theera-Umpon N. Phiphatkhunarnon P. Auephanwiriyakul S. Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
author_facet |
Theera-Umpon N. Phiphatkhunarnon P. Auephanwiriyakul S. |
author_sort |
Theera-Umpon N. |
title |
Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
title_short |
Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
title_full |
Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
title_fullStr |
Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
title_full_unstemmed |
Linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
title_sort |
linear prediction-based reconstruction of electrocardiogram with premature ventricular contraction for heart rate variability analysis |
publishDate |
2014 |
url |
http://www.scopus.com/inward/record.url?eid=2-s2.0-84880729696&partnerID=40&md5=fb4d39f2d37055f2af17f9db3fc92d35 http://cmuir.cmu.ac.th/handle/6653943832/1641 |
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1681419708328312832 |