R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices
R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices
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DOI：
10.14326/abe.7.28
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发表时间：
2018-01-01
影响因子：
1
通讯作者：
Yamada, Tomohiro
中科院分区：
文献类型：
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作者：
Eguchi, Kana;Aoki, Ryosuke;Yamada, Tomohiro
由可穿戴ECG设备捕获的心电图（ECG）由于测量故障而容易包含伪影。由于伪影和R波具有非常相似的频率特性，因此可能导致R波误检测或R-R间期（RRI）误计算。针对心率变异性（HRV）的准确分析问题，提出了一种新的RRI异常值处理方法，该方法包括RRI可靠性评估、RRI异常值排除和缺失RRI值补充3个步骤。在第一步骤中，该方法评估所有检测到的R波的测量状态，并基于两个R波的测量状态的组合的测量状态来计算RRI可靠性。由于我们的目标是在非医疗环境中使用的可穿戴ECG设备，因此该方法基于左心室肥大的阈值电位来评估R波，并将超过阈值的R波确定为伪影。因此，该方法将较低的可靠性设置为包含被评估为伪影的R波的RRI。在第二步中，该方法排除所有可靠性低的RRI作为离群值。这些步骤对于时域中的HRV测量可能是有效的，但是对于频域中的HRV测量分析是不够的。在频域分析心率变异性时，如果目标RRI包含缺失值，则对时间序列RRI数据进行重新采样可能会产生离群值。我们的方法相应地补充缺失的RRI之前，基于RRI特性的数据重新排序。我们假设RRI的连续变化遵循一个简单的公式，该公式由三个部分组成：直流电、低频和高频。我们的方法根据公式补充缺失值，该公式是从被视为已正确测量的RRI时间序列计算的。为了在将其应用于可穿戴设备记录的ECG之前确认该方法的有效性，我们使用通过添加噪声和伪影以打开ECG数据而人工生成的伪ECG来评估所有步骤。初步评估结果表明，所提出的方法优于传统的方法的精度的时域和频域测量的心率变异性。
Electrocardiograms (ECGs) captured by wearable ECG devices readily contain artifacts due to measurement faults. Since artifacts and R waves have quite similar frequency characteristics, R wave misdetection or R-R interval (RRI) miscalculation may result. Aiming at accurate analysis of heart rate variability (HRV), this paper proposes a new RRI outlier processing method consisting of three steps: evaluating RRI reliability, excluding RRI outlier, and complementing missing RRI. In the first step, the method evaluates the measurement status of all detected R waves and calculates RRI reliability based on the measurement status of a combination of the measurement status of two R waves. Since we target wearable ECG devices used in non-medical environment, the method evaluates R waves based on the threshold electric potential for left ventricular hypertrophy, and determines those exceeding the threshold as artifacts. The method accordingly sets lower reliability to RRIs containing R waves evaluated as artifacts. In the second step, the method excludes all RRIs with low reliability as outliers. These steps may be effective for HRV measures in the time domain, but are not sufficient for analyzing HRV measures in the frequency domain. Resampling the time series RRI data, which is essential for analyzing HRV in the frequency domain, may produce outliers if the target RRIs contain missing values. Our method accordingly complements missing RRIs before data resampling based on RRI characteristics. We postulate that consecutive changes in RRIs follow a simple formula consisting of three components: direct current, low frequency, and high frequency. Our method complements missing values according to the formula, which is calculated from RRIs time series regarded as having been properly measured. To confirm the effectiveness of the method before applying it to ECGs recorded by wearable devices, we evaluated all the steps using pseudo-ECGs generated artificially by adding noise and artifacts to open ECG data. Initial evaluation results showed that the proposed method outperformed conventional method regarding the precision of both time and frequency domain measures of HRV.