实时睡眠分期算法研究与应用系统开发
本文选题:睡眠分期 + 心率变异性 ; 参考:《电子科技大学》2017年硕士论文
【摘要】:越来越多的人开始遭遇睡眠问题,评价睡眠质量进而改善睡眠状况已经成为一大课题。准确的睡眠分期是客观评估睡眠质量和诊断睡眠相关疾病的基础,经典自动分期方法基本上是对脑电信号进行分析的。但是,脑电信号的记录操作复杂,成本高,电极的放置也会干扰人的正常睡眠,无法满足家庭睡眠监测场合的需要。人们提出用较为方便检测的生理参数去自动分析睡眠过程,相较传统脑电更具实用价值,利用压电感知床垫能实现人在睡眠时多个生理参数的长时间同步检测,对睡眠几乎没有任何干扰,可以在家庭中监测人们的真实睡眠状况,拥有良好的应用前景。床垫采集的是心冲击图(BCG)信号,包含心率、呼吸率和体动信息,但目前对BCG信号进行自动睡眠分期的准确率不高。本文的研究目的是基于BCG信号建立一种更加准确可靠的多参数自动分期算法,然后应用于实现的床垫式实时睡眠监护系统中,进行睡眠的实时监测。本文采用从BCG信号中计算出的心率、呼吸和体动序列进行自动睡眠分期,分为四个阶段,即觉醒期、浅度睡眠期、深度睡眠期和快速眼动睡眠期。由于不同睡眠阶段的心率波形形态较难区分,利用时变自回归模型进行心率变异性(HRV)的特征提取,进一步基于隐马尔可夫模型进行特征的自动分类识别。发现高频段极点相位与总功率的特征组合能够较好地区分各睡眠分期,并结合呼吸率和体动信息校正分期结果,能够有效地提高基于HRV的分期准确率。使用MIT-BIH数据库中的数据测试,比较文中算法和专家的分期结果,验证了建立的多参数分期算法的精度,识别率达到70.13%,且计算快速,可用于实时监测。本文设计并实现了床垫式实时睡眠监护系统,将本文建立的自动睡眠分期算法应用到该系统中,能在家庭环境中使用,实时监测人们的睡眠。系统基于压电感知床垫,将从BCG信号分离出的实时的心率、呼吸率和体动数据上传至服务器进行存储,完成自动睡眠分期,最终通过智能手机应用为用户提供实时睡眠信息展示、睡眠质量分析和改善建议等服务,以帮助人们提高睡眠质量。
[Abstract]:More and more people are experiencing sleep problems. It has become a major topic to evaluate sleep quality and improve sleep quality. Accurate sleep staging is the basis of objective evaluation of sleep quality and diagnosis of sleep related diseases. However, the recording of EEG signals is complicated and costly, and the placement of electrodes will interfere with normal sleep, which can not meet the needs of family sleep monitoring. More convenient physiological parameters are proposed to automatically analyze sleep process, which is more practical than traditional EEG. Using piezoelectric sensing mattress can realize long time synchronous detection of multiple physiological parameters during sleep. It can monitor people's real sleep condition in the family, and has good application prospect. The mattresses collected the BCG) signals of cardiogram, which included heart rate, respiration rate and body movement information, but the accuracy of automatic sleep staging of BCG signals was not high at present. The purpose of this paper is to establish a more accurate and reliable multi-parameter automatic staging algorithm based on BCG signal, and then apply it to the realization of the mattress real-time sleep monitoring system to monitor sleep in real time. In this paper, the heart rate, respiration and body motion sequences calculated from BCG signal are used to carry out the automatic sleep stages, which are divided into four stages, namely, wakefulness, shallow sleep, deep sleep and rapid eye movement sleep. Because it is difficult to distinguish the shape of heart rate waveform in different sleep stages, the time-varying autoregressive model is used to extract the feature of HRV, and then the hidden Markov model is used for automatic classification and recognition. It is found that the feature combination of pole phase and total power in high frequency band can distinguish the sleep stages well, and can effectively improve the accuracy of staging based on HRV by combining the results of respiration rate and volume motility information correction. By using the data test in MIT-BIH database and comparing the results of the algorithm and the expert, the accuracy of the multi-parameter staging algorithm is verified. The recognition rate is 70.133.And the calculation is fast and can be used for real-time monitoring. In this paper, a mattress type real-time sleep monitoring system is designed and implemented. The automatic sleep staging algorithm established in this paper is applied to the system, which can be used in the home environment and monitor people's sleep in real time. Based on piezoelectric sensing mattress, the system uploads the real-time heart rate, respiration rate and body motion data separated from BCG signal to the server for storage, and completes the automatic sleep stage. Finally, the smartphone application provides users with real-time sleep information display, sleep quality analysis and improvement advice to help people improve the quality of sleep.
【学位授予单位】:电子科技大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TP311.52;R740
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