【作者】 王亮；

【导师】 史锦珊； 张淑清；

【作者基本信息】 燕山大学 ， 测试计量技术及仪器， 2001， 硕士

【摘要】 本课题所研制的动态心电图检测分析软件能实时地记录和分析长期动态心电信号，若发现危急病兆，便能发出警报，提示患者或医生采取应急措施。在记录和分析结束后，还可以立即显示并打印分析报告。 实时分析的任务包括波形检测和心律失常检测两大部分。波形检测部分主要进行QRS波、P波、T波检测，ST段测量以及心率变异性分析。在波形检测的基础上，心律失常分析部分结合医学经验判断室上性和室性心律失常，并检出报警事件，以对患者进行实时监护。 在波形检测中，我们采用新兴的小坡变换法对QRS波进行识别，经MIT/BIH心电数据库验证，识别正确率大于98％。小波分析运算简便、性能稳定，所检测的参数准确可靠，为进一步识别其它波形打下良好的基础。P、T波检出由简易的幅度检测法实现，ST段用经典的J+X法进行测量，心率变异性分析将线性方法和非线性方法相结合，满足了实时分析和临床要求。 在心律失常分类中，室上性心律失常包括心动过速、心动过缓、停搏、室上性早搏和心律不齐，室性心律失常包括RouT、室性心动过速、早搏联律、早搏成对和偶发室性早搏。未归入以上几类的划为待定波，由医生分析确认，从而避免漏检。实时监护功能在出现严重的心动过速、心动过缓、停搏和室性早搏过频事件时，可以发出报警信息。 上述实时分析的实现软件由Visual C++6.0语言编写，具有运算快捷、准确可靠、功能齐全、界面友好、使用方便等优点。经MIT/BIH专用心电数据库验证效果良好，尤其是经秦皇岛市人民医院典型临床病例数据试验，对室上性心律失常分析总正确率大于95％，对室性心律失常分析总正确率大于92％。更多还原

【Abstract】 The detection and analysis software developed by the present graduate project can record real-time long-lasting dynamic electrocardiogram and analyses it. As soon as any emergent cardiac illness happens, the software will give an alarm to prompt patients/doctors to take necessary measures. When the record and analysis missions have been completed, an ultimate medical report can also be displayed and printed simultaneously. The real-time analysis task mainly includes waveform detection and arrhythmia classification. The waveform detection part fulfills QRS complex wave, P wave, T wave detection, ST segment measurement and HRV analysis. Based on the aforementioned wave detection, the arrhythmia classification part use clinical knowledge to judge SVA (Super-ventricular arrhythmia) and VA, and perform real-time monitoring function to check alarm events. In waveform detection, the newly arising technology of wavelet transforms has been employed to detect QRS complex waves. Detection accuracy is greater than 98% verified by MIT/BIH ECG database. Wavelet transforms method has multitude advantages including simple operation, reliable performance and precise measurement for characteristic parameters.. And accurate QRS detection has played a solid foundation for the other waveform identification. P and I waves detection can be realized by convenient magnitude-depended method. ST segment can be measurement by classical J+X method, and HRV analysis can be implemented by the combination of linear and nonlinear methods. All these detection approaches have met the requirement of real-time analysis and clinical diagnosis. In arrhythmia classification, SVA includes tachycardia, bradycardia, asystole, SVPB (super-ventricular premature beat) and rhythm disorders. VA includes RonT, ventricular tachycardia, VPB, coupled VPB, bigeminy and trigeminy. Other abnormal QRS named as unclassified waves are selected and will be confirmed by doctors to avoid missed ventricular arrhythmia. The real-time monitoring function will give out alarm information when serious tachycardia, bradycardia, asystole and frequent VPB are found. ?II ? Abstract The real-time implementation software above is programmed with Visual C++ 6.0. It is well-known for fast operation, high accuracy, perfect function, friendly interface and convenient usage. The software has presented good performance tested by MIT/BIH ECG database. The clinical experiment in the People抯 Hospital of Qinhuangdao shows that the total detection accuracy for SVA is greater than 95%, for VA is greater than 92%.更多还原