College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.
Physical Education College of Jilin University, Changchun, China.
Comput Math Methods Med. 2019 Dec 30;2019:2067196. doi: 10.1155/2019/2067196. eCollection 2019.
Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.
脉搏信号被广泛用于评估人体心血管、呼吸和循环系统的状态。在采集过程中，信号通常会受到一些因素的干扰，例如尖峰噪声和传感器接触不良噪声，这些因素严重影响了后续检测模型的准确性。近年来，已经应用了一些方法来处理上述噪声信号，例如动态时间规整、经验模态分解、自相关和互相关。虽然这些方法很有效，但它们复杂且难以实现。此外，还发现噪声信号与粗大误差密切相关。Chauvenet 准则是粗大误差判别准则之一，由于没有分解和重构等复杂计算，因此效率高，适用范围广。因此，在本研究中，基于 Chauvenet 准则，提出了一种新的脉搏信号预处理方法，分别设计自适应阈值来区分由尖峰噪声和传感器接触不良噪声引起的异常信号。本研究使用了来自麻省理工学院生物医学工程研究所多导睡眠图数据库的 81 小时脉搏信号（每 30 秒标注一次睡眠呼吸暂停，总共 9720 个片段），包括 35 分钟的传感器接触不良噪声和 25 分钟的尖峰噪声。所提出的方法用于预处理脉搏信号，其中在总共 9720 个片段中，正确区分了 9684 个片段，方法的准确率达到了 99.63%。为了定量评估噪声去除效果，进行了仿真实验，分别比较了噪声去除前后计算的 Jaccard 相似系数（JSC），结果表明预处理信号获得了更高的 JSC，更接近参考信号，这表明所提出的方法可以有效提高信号质量。为了评估该方法，分别建立了三个具有相同网络结构和参数的反向传播（BP）睡眠呼吸暂停检测模型，通过比较模型的识别率和预测率，使用所提出的方法获得了更高的率。为了证明效率，进行了基于 Chauvenet 的方法与基于 Romanovsky 的方法的对比实验，所提出的方法的执行时间明显短于 Romanovsky 方法。结果表明，随着数据量的增加，Chauvenet 方法在执行时间上的优势变得更加显著。
