通过将表2与其他文章所分析的信号特征和采用的算法进行对比，特征在本文中

By comparing Table 2 with the signal characteristics analyzed in other articles and the algorithms used, the characteristics are extracted based on the BCG signal and body motion signal in this article. The comprehensive recognition accuracy rate is 83.14%, and a good recognition effect is achieved. <br><br><br>Discussion and conclusion The <br>quality of sleep directly affects people's normal life and physical and mental health. In this paper, the BP neural network algorithm based on BCG signal and body motion signal has a high consistency between the discrimination results of sleep and wakefulness and the calibration, especially the correct recognition of sleep. <br>In order to improve the recognition speed, heart rate variability and body movement values ​​are selected as the characteristic values, the calculation amount is greatly reduced, and the recognition speed is improved. At the same time, the time-frequency domain characteristics of the heart rate variability are used to reduce the environmental interference caused by the direct use of the cardiac shock signal and improve the robustness of the algorithm. The use of heart rate variability and body movement values ​​can better feed back the characteristics of different stages of sleep, and the process of collecting heart shock signals and body movement signals is more convenient. Of course, when it is applied to thousands of people, the recognition error may become larger. It is necessary to continue to study the extraction of more characteristic parameters from the heart shock signal and optimize the neural network model to further improve the recognition rate of sleep staging. <br>This article uses cardiac shock signals and body motion signals to provide a simple and effective method for the identification of sleep stages. The method combined with wearable devices can be used for home sleep monitoring and can also be used as a preliminary screening for sleep diseases. It can be used as a preliminary screening diagnosis of sleep diseases in clinical medicine, and it can also facilitate users to monitor sleep quality at home; moreover, it can better solve the problem of polysomnography affecting normal sleep during the monitoring process. <br><br><br><br>references

通过将表2与其他文章所分析的信号特征和采用的算法进行对比，特征在本文中被基于BCG信号和体动信号进行提取，综合的识别准确率为83.14%，取得较好的识别效果。<br><br><br>讨论与结论<br>睡眠质量的好坏直接影响人们正常生活和身心健康。本文采用基于BCG信号和体动信号的BP神经网络算法对睡眠、觉醒的判别结果与标定的一致性较高，尤其是对睡眠的正确识别。<br>为了提高识别速度，选取了心率变异性和体动值作为特征值，计算量大大减少，识别速度得以提高。同时，利用心率变异性的时频域特性减少直接采用心冲击信号带来的环境干扰，提高算法的鲁棒性。利用心率变异性和体动值能够较好地反馈睡眠不同阶段的特征，同时心冲击信号和体动信号的采集过程较为方便。当然，应用到成千上万个人的时候，可能会识别误差会变大，需要继续研究从心冲击信号中提取更多特征参数以及优化神经网络模型来进一步提高睡眠分期的识别率。<br>本文利用心冲击信号和体动信号为睡眠分期的识别提供一种较为简便有效的方法。该方法结合穿戴式设备，可用于家庭睡眠监测，也可作为睡眠疾病的初筛。可以作为临床医学上睡眠疾病的初筛诊断，也可以方便用户居家监测睡眠质量情况；而且，较好地解决多导睡眠仪在监测过程中影响正常睡眠的问题。<br><br><br><br>参考文献

By comparing the signal features and algorithms analyzed in Table 2 and other articles, the features are extracted based on BCG signal and body motion signal in this paper, and the comprehensive recognition accuracy rate is 83.14%, and good recognition effect is achieved.<br>Discussion and conclusion<br>The quality of sleep directly affects people's normal life and physical and mental health. In this paper, BP neural network algorithm based on BCG signal and body motion signal is used to distinguish sleep and wakefulness, which is consistent with the calibration results, especially the correct recognition of sleep.<br>In order to improve the recognition speed, heart rate variability and body movement value are selected as the characteristic values, which greatly reduces the amount of calculation and improves the recognition speed. At the same time, the time-frequency characteristics of HRV are used to reduce the environmental interference caused by direct sampling impulse signal and improve the robustness of the algorithm. Heart rate variability (HRV) and body movement value (SV) can be used to feed back the characteristics of different sleep stages. At the same time, the acquisition process of cardiac impulse signal and body movement signal is more convenient. Of course, when it is applied to thousands of people, the recognition error may become larger. It is necessary to continue to study the extraction of more feature parameters from cardiac shock signal and optimize the neural network model to further improve the recognition rate of sleep staging.<br>This paper provides a simple and effective method for the recognition of sleep stages by heart impulse signal and body movement signal. This method, combined with wearable devices, can be used for home sleep monitoring and screening of sleep diseases. It can be used as the preliminary screening and diagnosis of sleep diseases in clinical medicine, and it can also facilitate users to monitor sleep quality at home; moreover, it can better solve the problem that polysomnography affects normal sleep in the monitoring process.<br>reference