【作者】 李立策；

【导师】 郭兴明；

【作者基本信息】 重庆大学 ， 生物医学工程， 2008， 硕士

【摘要】 远程医疗系统为如今社会医疗资源紧缺的问题提供了一种有效的解决方法。然而由于系统的家庭终端部分存在着不够方便和测量过程不舒适等问题,使得远程医疗系统得不到普及。本论文研究的穿戴式远程医疗系统是一种新颖的医疗仪器,使患者无需到医院就得到日常监护。本论文重点研究了穿戴式远程医疗系统客户端的心电信号检测算法,提出了符合系统实时性要求的ECG信号R波和P波检测算法。首先对ECG信号进行预处理,采用平滑滤波器来滤除50Hz工频干扰,然后对信号进行小波变换,通过将高尺度的逼近信号和低尺度的细节信号置零并重构信号,达到去除基线漂移和高频噪声的目的。接着本文重点研究了心电特征信息提取方法。在分析了小波变换检测心电信号奇异点的原理之后,采用Daubechies3阶小波对预处理后的ECG信号进行分解,在22和23两个小波系数上使用自适应阈值、不应期和补偿等策略对心电信号进行R波检测。实验结果显示,算法的平均正确检测率达到了99%以上。P波检测的方法是在R波定位完成之后,将QRS-T波对消掉,使得原始信号仅剩P波为主要成分,然后对这部分信号进行自适应阈值判断,检测出P波位置。实验结果显示本算法对某些信号的检测效率达到了90%。另外本文还研究了部分心律失常病例的自动分析技术,介绍了心动过缓、心动过速等病例的判断指标。课题使用TMS320VC5509DSP处理器作为算法实现平台,它具有运行功率低,计算能力强的特点,非常适合应用于穿戴式设备。本文最后介绍了CCS集成开发环境测试检测算法性能的方法,通过profile工具计算执行时间可以得知算法的执行效率。实验结果表明R波检测和P波检测算法在TMS320VC5509DSP处理器上可以高效率运行。更多还原

【Abstract】 Telemedicine system have provided an effective solution to tackle the problem of scarcity in medical resources for the society. Due to the inconvenient and uncomfortable process of measurement in system’s client, which make telemedicine system cannot be widely put into practice. In this paper, a new medical device that called wearable telemedicine system is introduced. Without going to hospital, patient can take daily care service under the help of this system. Meanwhile, the ECG signal detection algorithms on the client of wearable telemedicine systems are discussed, and then a R-wave detection and P-wave detection algorithm are proposed for real-time detecting.First, the pre-processing procedure include: a smooth filter to filter 50 Hz frequency interference, wavelet transform to remove baseline drift and high frequency noise (mainly through set high-scale approximation coefficient and low-scale detail coefficient to reconstruct signal).Then this paper focus on the ECG feature extraction methods. After analyzing the principle of wavelet transform in ECG signal singular point detection, db3 wavelet is used to decompose the signal, and then self-adaptive threshold, refractory period and compensation strategies are put into practice to detect R wave on 22 and 23 wavelet coefficients. The experiments’results show that average correct detection rate of this algorithm is above 99%.Based on this algorithm, P wave can be detected by self-adaptive threshold estimation after attenuating the amplitude of QRS-T peak in order to highlight the energy of P wave. Similarly, the experiments results show that detection rate of this algorithm in some signals can reach 90%. Besides, this paper dive into automatic analysis technology of some arrhythmia cases. The indicators of bradycardia and tachycardia have been introduced.In this project the ECG signal have been processed on the client of wearable telemedicine system, in which kernel chip is TMS320VC5509DSP processor. This processor with low power and strong calculation ability has been regarded as suitable for wearable medical device. Finally, the method which used to test algorithm in CCS IDE have been elaborated. Profile tool is able to calculate the operating time from which the operation efficiency of algorithm can be obtained. Experiment results show that R-wave and P-wave detection algorithm can be operated in TMS320VC5509DSP processor efficiently.更多还原