Artifact removal is probably the most important and (unfortunately) most overlooked step of the signal processing pipeline required to compute HRV features

一个rtifact去除可能是最重要的，(可惜)最容易被忽视需要计算HRV功能的信号处理管道的步骤

While all beat to beat data should go through artifact removal (even when collected with ECG or chest straps, as ectopic beats would still be present under these circumstances, see an example here), the issue becomes particularly important for PPG measurements, as they are more prone to noise (which means that it’s easier to mess up the signal, just by moving)

尽管所有心跳数据都应通过伪影去除(即使使用心电图或胸带收集，因为在这种情况下仍会出现异位心跳，请参见此处的示例)，但对于PPG测量而言，这个问题变得尤为重要更容易产生噪声(这意味着仅通过移动就更容易弄乱信号)

The issue of artifact removal is particularly important for HRV analysis. In particular, even a single artifact over a 5 minutes window, can have very large consequences in terms of the derived HRV features (we’ll see an example in a minute). Camera-based apps, watches, rings, wristbands, are all affected by these issues as soon as you move

去除伪影的问题对于HRV分析尤其重要。 特别是，即使在5分钟的窗口内只有一个工件，也可能在派生的HRV功能方面产生非常大的后果(我们将在稍后看到一个示例)。 一旦您移动，基于相机的应用程序，手表，戒指，腕带都会受到这些问题的影响

Thus, if our goal is to correctly compute HRV features in healthy individuals (i.e. individuals without cardiac issues), we need to do our best to clean the beat to beat intervals from any artifacts, regardless of their origin (actual ectopic beats, or issues because the user was moving)

因此，如果我们的目标是正确计算健康个体(即无心脏问题的个体)的HRV特征，则我们需要尽力清除节拍以消除任何伪影的间隔，无论其来源如何(实际异位搏动或问题)因为用户在移动)

问题有多严重？ (How bad is the problem?)

Really bad

特别糟糕

This is why 99% of smartwatches on the market do not even bother with HRV analysis and target only heart rate estimation, which still doesn’t really work consistently when exercising (if you are serious about your exercise heart rate, please get a chest strap)

这就是为什么市场上99％的智能手表都不会为心率变异性分析而烦恼，而只针对心率估算，这在运动时仍然无法始终如一地工作(如果您对运动心率很认真，请系好胸带)

The few devices that do go through the trouble of doing HRV analysis, normally do so while you sleep (e.g. an Oura ring or Fitbit), or using a specific sensing modality (e.g. the Scosche Rhythm24 HRV mode or the Apple Watch using the Breathe app). This choice makes a lot of sense as if you are sleeping or doing a breathing exercise, you are not moving that much.

很少有遇到HRV分析麻烦的设备，通常会在您睡觉时(例如，Oura环或Fitbit)或使用特定的感应方式(例如，Scosche Rhythm24 HRV模式或使用“呼吸”应用程序的Apple Watch)进行)。 这种选择很有意义，就好像您正在睡觉或进行呼吸运动时，您并没有动那么多。

Additionally, given the limited utility of HRV analysis during exercise, as long as you are able to collect high-quality data at rest or during the night, you are good to go (you can learn more about heart rate variability and when to measure, in our guide here).

此外，鉴于运动期间HRV分析的作用有限，只要您能够在休息或夜间收集高质量的数据，就可以继续进行(您可以了解有关心率变异性以及何时进行测量的更多信息，我们的导游在这里)。

For phone or camera-based measurements, similar to the ones we use in HRV4Training or HRV4Biofeedback, issues are typically caused by finger movement, as the apps are used while at rest, and therefore there is no body movement

对于基于电话或摄像头的测量，类似于我们在HRV4Training或HRV4Biofeedback中使用的测量，问题通常是由手指移动引起的，因为应用程序在静止时使用，因此没有身体移动

Let’s look at one example:

让我们看一个例子：