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首页> 外文期刊>BioMedical Engineering OnLine >Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data
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Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data

机译:在动态心电图记录中分析心率变异性之前,先自动过滤掉RR间隔中的异常值:与精心编辑的数据进行比较

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Background 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. Methods and Results 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. Conclusions 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. 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.
机译:背景技术未检测到的心律失常会严重影响心率变异性(HRV)的功率谱。因此,通常在分析HRV之前仔细编辑一系列RR间隔,但这在分析24小时录音时是一个耗时的过程。可替代地,可以使用不同的方法来自动去除心律不齐的搏动和伪像。这项研究比较了根据手动编辑和自动过滤的RR间隔确定的HRV的常见频域指标。方法和结果可从140名1-75岁的健康受试者中获得24小时动态心电记录。经验丰富的技术人员精心编辑了所有录音。使用递归程序执行自动过滤,其中如果RR间隔与周围RR间隔的平均值相差超过预定极限(范围为10%至5​​0%),则将RR间隔删除。通过用合成异位搏动替换1%的搏动来评估过滤算法。在过滤原始编辑数据和噪声数据集之前和之后进行功率谱分析。使用嘈杂数据的分析结果用于定义基于年龄的过滤阈值。使用完全未经编辑的数据评估基于年龄的过滤,方法是从一系列RR间隔中删除所有注释,然后将所得的HRV指数与使用经编辑的数据获得的HRV指数进行比较。结果显示,按年龄过滤后,编辑过的和未编辑过的数据集具有相同的结果,与每个年龄段的平均值相比,通过不同的预处理方法获得的HRV指数差异很小。结论研究表明,在Holter记录中对HRV进行分析之前,可能不必对所有检测到的心跳进行费时的仔细编辑。在大多数受试者中,执行有效的心律不齐分析所需的常规编辑就足够了,然后通过基于年龄的一系列RR间隔过滤来去除未检测到的异位搏动和伪像,尤其是在30岁以上的受试者中。

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