IEEE Trans Biomed Eng. 2022 Sep;69(9):2982-2993. doi: 10.1109/TBME.2022.3158582. Epub 2022 Aug 19.
With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals.
We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments.
A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work.
As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.
随着可穿戴医疗设备在远程患者监测中的应用日益广泛，需要可靠的信号质量评估（SQA）来确保对患者记录数据的解释和诊断具有高精度。可穿戴设备无创测量的光电容积脉搏波（PPG）信号被广泛用于提供有关心血管系统及其相关疾病的信息。在本研究中，我们提出了一种优化 PPG 信号质量评估的方法。
我们使用基于集成的特征选择方案来提高分类模型的预测性能，以评估 PPG 信号的质量。我们的特征和子集大小选择方法产生了最合适的特征子集，该子集经过优化后可区分干净和有伪影的 PPG 段。
通过所提出的特征选择方法，在测试数据上实现了两个类之间的高区分能力，从而在所有依赖和独立测试数据集上都取得了良好的性能。对于依赖测试数据集，我们实现了高于 0.93 的准确率、敏感性和特异性，分别为心房颤动（AF）或包括正常窦性节律（NSR）、房性早搏（PAC）和室性早搏（PVC）在内的非 AF 数据的独立于心搏类型；对于独立测试数据集，在从 AF 和非 AF 患者记录的 PPG 数据上，准确率、敏感性和特异性分别大于 0.93、0.89 和 0.97。这些结果被发现比本工作中引用的所有当代方法都更准确。
正如结果所表明的，我们提出的方案的优势在于其在长期 14 天记录期间对 PPG 信号的动态变化具有鲁棒性，同时伴随着不同类型的身体活动以及由不同个体血液动力学特征引起的各种波动和波形，以及各种类型的记录设备。这种鲁棒性使该算法在各种可穿戴设备中的应用充满信心，成为一种可靠的 PPG 信号质量评估方法。
