Department of Physics, University of Ngaoundere, Ngaoundere, Cameroon.
Department of Physics, University of Bertoua, Bertoua, Cameroon.
Biomed Phys Eng Express. 2023 Aug 3;9(5). doi: 10.1088/2057-1976/ace9e0.
As the current healthcare system faces problems of budget, staffing, and equipment, telemedicine through wearable devices gives a means of solving them. However, their adoption by physicians is hampered by the quality of electrocardiogram (ECG) signals recorded outside the hospital setting. Due to the dynamic nature of the ECG and the noise that can occur in real-world conditions, Signal Quality Assessment (SQA) systems must use robust signal quality indices (SQIs). The aim of this study is twofold: to assess the robustness of the most commonly used SQIs and to report on their complexity in terms of computational speed. A total of 39 SQIs were explored, of which 16 were statistical, 7 were non-linear, 9 were frequency-based and 7 were based on QRS detectors. With 6 databases, we manually constructed 2 datasets containing many rhythms. Each signal was labelled as 'acceptable' or 'unacceptable' (subcategories: 'motion artefacts', 'electromyogram noise', 'additive white Gaussian noise', or 'power line interference'). Our results showed that the performance of an SQI in distinguishing a good signal from a bad one depends on the type of noise. Furthermore, 23 SQIs were found to be robust. The analysis of their extraction time on 10-second signals revealed that statistics-based and frequency domain-based SQIs are the least complex with an average computational time of (mean: 1.40 ms, standard deviation: 1.30 ms), and (mean: 4.31 ms, standard deviation: 4.50 ms), respectively. Then, our results provide a basis for choosing SQIs to develop more general and faster SQAs.
随着当前医疗保健系统面临预算、人员配备和设备等问题，可穿戴设备的远程医疗提供了一种解决这些问题的手段。然而，由于医院环境外记录的心电图 (ECG) 信号质量，医生对其采用受到了阻碍。由于 ECG 的动态性质以及实际条件下可能出现的噪声，信号质量评估 (SQA) 系统必须使用稳健的信号质量指标 (SQI)。本研究旨在评估最常用的 SQI 的稳健性，并报告其在计算速度方面的复杂性。共探索了 39 个 SQI，其中 16 个是统计的，7 个是非线性的，9 个是基于频率的，7 个是基于 QRS 检测器的。我们使用 6 个数据库，手动构建了包含许多节律的 2 个数据集。每个信号都被标记为“可接受”或“不可接受”（子类别：“运动伪影”、“肌电图噪声”、“附加白高斯噪声”或“电源线干扰”）。我们的结果表明，SQI 区分良好信号和不良信号的性能取决于噪声类型。此外，我们发现有 23 个 SQI 具有稳健性。对 10 秒信号提取时间的分析表明，基于统计和基于频域的 SQI 是最不复杂的，平均计算时间分别为（均值：1.40 毫秒，标准差：1.30 毫秒）和（均值：4.31 毫秒，标准差：4.50 毫秒）。然后，我们的结果为选择 SQI 提供了依据，以开发更通用和更快的 SQA。
