Biomechanics and Bioengineering Lab, University of Technology of Compiègne (UMR CNRS 7338), 60200 Compiègne, France.
Heudiasyc Lab (Heuristics and Diagnosis of Complex Systems), University of Technology of Compiègne (UMR CNRS 7338), 60200 Compiègne, France.
Sensors (Basel). 2022 Mar 3;22(5):1984. doi: 10.3390/s22051984.
Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data.
得益于与人工智能算法结合的可穿戴设备，可以在非卧床环境中记录和分析心率变异性（HRV）等生理参数。这种设置的主要缺点是由于运动、噪声和数据丢失，记录数据的质量较差。这些错误可能会极大地改变 HRV 分析，因此应该事先解决，特别是如果用于医疗诊断。一种广泛使用的处理此类问题的方法是插值，但这种方法不能保留信号的时间依赖性。在本研究中，我们提出了一种新的 HRV 处理方法，包括使用高斯分布进行滤波和迭代数据插补。该方法的特点是考虑了许多生理方面，如 HRV 分布、RR 变异性和正常边界，以及时间序列特征。我们使用随机森林分类器（RF）研究了该方法对分类的影响，并将其与其他数据插补方法（包括线性、保形分段三次 Hermite（pchip）和样条插值）进行比较，在应激案例研究中。使用所有四种方法对 67 名健康受试者的重建 HRV 信号进行了特征分析，并通过随机森林算法分别进行分类，以检测应激与放松。即使在缺失数据百分比较高的情况下，所提出的方法也达到了稳定的 F1 得分为 61%，而其他插值方法在缺失数据百分比较低的情况下达到了约 54%的 F1 得分，当百分比增加时，性能下降到约 44%。这表明，我们的方法在应激分类方面的效果更好，尤其是在缺失数据百分比较高的信号上。
