Heart Rate Variability Analysis: How Much Artifact Can We Remove?
Heart Rate Variability Analysis: How Much Artifact Can We Remove?
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DOI：
10.30773/pi.2020.0168
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发表时间：
2020-09
影响因子：
2.7
通讯作者：
Baker SD
中科院分区：
文献类型：
--
作者：
Sheridan DC;Dehart R;Lin A;Sabbaj M;Baker SD
心率变异性 (HRV) 评估心脏产生的微小心跳时间间隔 (BBI) 差异，建议将其作为自主神经系统的标志。手腕佩戴设备运动产生的伪影会显着影响 HRV 分析的有效性。本研究的目的是确定 BBI 选择中的小错误对 HRV 分析的影响，并为心理健康可穿戴技术的未来研究奠定基础。 这是在 ClinicalTrials.gov 注册的前瞻性观察性临床试验 (NCT03030924) 的子分析。研究团队操纵了 10 名受试者在没有任何伪影的情况下通过可穿戴手腕监视器获得的 HRV 追踪数据，以代表遇到的最常见的伪影形式。 当在错误的时间间隔选择多达 5 个心跳并且去除多达 36% 的 BBI 时，连续差异的均方根保持在临床显着变化以下。当在错误的时间间隔选择最多 3 个心跳并且删除高达 36% 的 BBI 时，下一个正常间隔的标准差保持在临床显着变化以下。当在错误的时间间隔选择超过 2 个心跳并且删除任何 BBI 时，高频 HRV 显示出显着变化。 与频域相比，时域 HRV 指标似乎对伪影更加稳健。检查可穿戴技术对心理健康的研究人员应该了解这些值，以便将来分析 HRV 研究以提高数据质量。
Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology. This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered. Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed. Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.
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影响因子：
--
作者：
Georgiou, Konstantinos;Larentzakis, Andreas V;Giallafos, Elias J
通讯作者：
Giallafos, Elias J
DOI：
10.3390/s18030844
发表时间：
2018-03-13
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Vescio B;Salsone M;Gambardella A;Quattrone A
通讯作者：
Quattrone A
影响因子：
5.4
作者：
Goldenberg, Ilan;Goldkorn, Ronen;Johnson, Bruce
通讯作者：
Johnson, Bruce
影响因子：
2.3
作者：
Scholkmann, Felix;Boss, Jens;Wolf, Martin
通讯作者：
Wolf, Martin
影响因子：
5.2
作者：
Shaffer F;Ginsberg JP
通讯作者：
Ginsberg JP