IEEE J Biomed Health Inform. 2023 May;27(5):2323-2333. doi: 10.1109/JBHI.2021.3138639. Epub 2023 May 4.
Heart rate variability (HRV) has been used in assessing mental workload (MW) level. Compared with ECG, photoplethysmogram (PPG) provides convenient in assessing MW with wearable devices, which is more suitable for daily usage. However, PPG collected by smartwatches are prone to suffer from artifacts. Those signal corruptions cause invalid Inter-beat Intervals (IBI), making it challenging to evaluate the HRV feature. Hence, the PPG-based MW assessment system is difficult to obtain a sustainable and reliable assessment of MW. In this paper, we propose a pre- and post- processing technique, called outlier removal and uncertainty estimation, respectively, to reduce the negative influences of invalid IBIs. The proposed method helps to acquire accurate HRV features and evaluate the reliability of incoming IBIs, rejecting possibly misclassified data. We verified our approach in two open datasets, which are CLAS and MAUS. Experiment results show proposed method achieved higher accuracy (66.7% v.s. 74.2%) and lower variance (11.3% v.s. 10.8%) among users, which has comparable performance to an ECG-based MW system.
心率变异性（HRV）已被用于评估心理工作量（MW）水平。与心电图（ECG）相比，光电容积脉搏波（PPG）可通过可穿戴设备方便地评估 MW，更适合日常使用。然而，智能手表采集的 PPG 容易受到伪影的影响。这些信号干扰会导致无效的心跳间隔（IBI），从而难以评估 HRV 特征。因此，基于 PPG 的 MW 评估系统难以对 MW 进行持续可靠的评估。在本文中，我们提出了一种预处理和后处理技术，分别称为异常值去除和不确定性估计，以减少无效 IBI 的负面影响。所提出的方法有助于获取准确的 HRV 特征，并评估传入 IBI 的可靠性，拒绝可能的错误分类数据。我们在两个公开数据集 CLAS 和 MAUS 中验证了我们的方法。实验结果表明，所提出的方法在用户中的准确率（66.7% 对 74.2%）和方差（11.3% 对 10.8%）方面均有提高，与基于 ECG 的 MW 系统具有可比的性能。
