Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data
<p>Abstract</p> <p>Background</p> <p>Undetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2012-01-01
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| Series: | BioMedical Engineering OnLine |
| Online Access: | http://www.biomedical-engineering-online.com/content/11/1/2 |
| _version_ | 1818156783071395840 |
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| author | Karlsson Marcus Hörnsten Rolf Rydberg Annika Wiklund Urban |
| author_facet | Karlsson Marcus Hörnsten Rolf Rydberg Annika Wiklund Urban |
| author_sort | Karlsson Marcus |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Undetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure when 24-hours recordings are analysed. Alternatively, different methods can be used for automatic removal of arrhythmic beats and artefacts. This study compared common frequency domain indices of HRV when determined from manually edited and automatically filtered RR intervals.</p> <p>Methods and Results</p> <p>Twenty-four hours Holter recordings were available from 140 healthy subjects of age 1-75 years. An experienced technician carefully edited all recordings. Automatic filtering was performed using a recursive procedure where RR intervals were removed if they differed from the mean of the surrounding RR intervals with more than a predetermined limit (ranging from 10% to 50%). The filtering algorithm was evaluated by replacing 1% of the beats with synthesised ectopic beats. Power spectral analysis was performed before and after filtering of both the original edited data and the noisy data set. The results from the analysis using the noisy data were used to define an age-based filtering threshold. The age-based filtration was evaluated with completely unedited data, generated by removing all annotations from the series of RR intervals, and then comparing the resulting HRV indices with those obtained using edited data. The results showed equivalent results after age-based filtration of both the edited and unedited data sets, where the differences in HRV indices obtained by different preprocessing methods were small compared to the mean values within each age group.</p> <p>Conclusions</p> <p>The study showed that it might not be necessary to perform the time-consuming careful editing of all detected heartbeats before HRV is analysed in Holter recordings.</p> <p>In most subjects, it is sufficient to perform the regular editing needed for valid arrhythmia analyses, and then remove undetected ectopic beats and artefacts by age-based filtration of the series of RR intervals, particularly in subjects older than 30 years.</p> |
| first_indexed | 2024-12-11T15:03:47Z |
| format | Article |
| id | doaj.art-bff08246bc414fd9aeb5b1b8febd5b59 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-11T15:03:47Z |
| publishDate | 2012-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-bff08246bc414fd9aeb5b1b8febd5b592022-12-22T01:01:01ZengBMCBioMedical Engineering OnLine1475-925X2012-01-01111210.1186/1475-925X-11-2Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited dataKarlsson MarcusHörnsten RolfRydberg AnnikaWiklund Urban<p>Abstract</p> <p>Background</p> <p>Undetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure when 24-hours recordings are analysed. Alternatively, different methods can be used for automatic removal of arrhythmic beats and artefacts. This study compared common frequency domain indices of HRV when determined from manually edited and automatically filtered RR intervals.</p> <p>Methods and Results</p> <p>Twenty-four hours Holter recordings were available from 140 healthy subjects of age 1-75 years. An experienced technician carefully edited all recordings. Automatic filtering was performed using a recursive procedure where RR intervals were removed if they differed from the mean of the surrounding RR intervals with more than a predetermined limit (ranging from 10% to 50%). The filtering algorithm was evaluated by replacing 1% of the beats with synthesised ectopic beats. Power spectral analysis was performed before and after filtering of both the original edited data and the noisy data set. The results from the analysis using the noisy data were used to define an age-based filtering threshold. The age-based filtration was evaluated with completely unedited data, generated by removing all annotations from the series of RR intervals, and then comparing the resulting HRV indices with those obtained using edited data. The results showed equivalent results after age-based filtration of both the edited and unedited data sets, where the differences in HRV indices obtained by different preprocessing methods were small compared to the mean values within each age group.</p> <p>Conclusions</p> <p>The study showed that it might not be necessary to perform the time-consuming careful editing of all detected heartbeats before HRV is analysed in Holter recordings.</p> <p>In most subjects, it is sufficient to perform the regular editing needed for valid arrhythmia analyses, and then remove undetected ectopic beats and artefacts by age-based filtration of the series of RR intervals, particularly in subjects older than 30 years.</p>http://www.biomedical-engineering-online.com/content/11/1/2 |
| spellingShingle | Karlsson Marcus Hörnsten Rolf Rydberg Annika Wiklund Urban Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data BioMedical Engineering OnLine |
| title | Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data |
| title_full | Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data |
| title_fullStr | Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data |
| title_full_unstemmed | Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data |
| title_short | Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data |
| title_sort | automatic filtering of outliers in rr intervals before analysis of heart rate variability in holter recordings a comparison with carefully edited data |
| url | http://www.biomedical-engineering-online.com/content/11/1/2 |
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