MEMRS-ECG心电网络系统与常规心电图仪在临床诊断中的差异性分析
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摘要
目的:比较分析MEMRS-ECG心电网络系统与常规心电图仪在心电图采集及诊断方面的差异性。方法:随机选取住院治疗的216例患者,将采用MEMRS-ECG心电网络系统进行心电图检测的患者纳入观察组,采用常规心电图仪进行心电图检测的患者纳入对照组,每组108例。统计两组患者的诊断准确率、患者等待时间(T1)和医生等待时间(T2)、心电图数据以及患者和医护人员的满意度。结果:观察组的诊断正确率为95.37%,高于对照组的86.11%,其差异有统计学意义(x~2=5.510,P<0.05);观察组的患者等待时间(T1)和医生等待时间(T2)均明显少于对照组,其差异有统计学意义(t=31.823,t=24.327;P<0.05);两组患者心电图所显示的心率、P波时限、PR间期以及T波时限之间无统计学差异,而观察组患者的QRS时限明显低于对照组,其差异有统计学意义(t=24.249,P<0.05);观察组患者及医护人员对心电图检测的满意度明显高于对照组,其差异有统计学意义(t=8.217,t=7.435;P<0.05)。结论:MEMRS-EGG心电网络系统是一种快捷、准确率高的心电图检测技术,能够提高医院诊断效率及患者对医疗服务的满意度。
Objective: To compare and analyze the differences of electrocardiogram collection and diagnosis between MEMRS ECG network system and conventional electrocardiography. Methods: 216 patients who received treatment in hospital were randomly selected, and patients who received MERS-ECG network system to implement detection were divided into observation group(108 cases), and ones who received conventional electrocardiography to implement detection were divided into control group(108 cases). The diagnostic accuracy, waiting time of patients(T1) and waiting time of doctors(T2), electrocardiogram data, and the satisfactions of patients and medical staffs were calculated as statistic method. Results: The diagnostic accuracy of observation group(95.37%) was significantly higher than that of control group(86.11%)(x2=5.510, P<0.05). And the T1 and T2 of observation group were significantly shorter than those of control group(t=31.823, t=24.327, P<0.05). There were no statistically significant difference in heart rate, P-wave duration, PR interval and T-wave duration between the two groups, while QRS duration of observation group was significantly lower than that of control group(t=24.249, P<0.05). The satisfactions of patients and medical staffs about the electrocardiogram test of observation group was significantly higher than that of control group(t=8.217, t=7.435, P<0.05). Conclusion: MEMRS-EGG network system is a fast and highly accurate ECG test technique, and it can improve the diagnostic efficiency of hospital and satisfaction of patients for medical service.
Objective: To compare and analyze the differences of electrocardiogram collection and diagnosis between MEMRS ECG network system and conventional electrocardiography. Methods: 216 patients who received treatment in hospital were randomly selected, and patients who received MERS-ECG network system to implement detection were divided into observation group(108 cases), and ones who received conventional electrocardiography to implement detection were divided into control group(108 cases). The diagnostic accuracy, waiting time of patients(T1) and waiting time of doctors(T2), electrocardiogram data, and the satisfactions of patients and medical staffs were calculated as statistic method. Results: The diagnostic accuracy of observation group(95.37%) was significantly higher than that of control group(86.11%)(x2=5.510, P<0.05). And the T1 and T2 of observation group were significantly shorter than those of control group(t=31.823, t=24.327, P<0.05). There were no statistically significant difference in heart rate, P-wave duration, PR interval and T-wave duration between the two groups, while QRS duration of observation group was significantly lower than that of control group(t=24.249, P<0.05). The satisfactions of patients and medical staffs about the electrocardiogram test of observation group was significantly higher than that of control group(t=8.217, t=7.435, P<0.05). Conclusion: MEMRS-EGG network system is a fast and highly accurate ECG test technique, and it can improve the diagnostic efficiency of hospital and satisfaction of patients for medical service.
引文
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[15]皇甫立夏,陆宇宏,施琦,等.基于.NET的心电网络系统的设计与实现[J].中国医学装备,2018,15(10):103-105.
[16]Scalvini S,Zanlelli E,Martinelli G,et al.Cardiac event recording yields more diagnoses than 24-hour Holter monitoring in patients with palpitations[J].JTelemed Telecare,2005,11(suppl 1):14-16.
[17]Zhang F,Lian Y.QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks[J].IEEE Trans Biomed Circuits Systems,2009,3(4):220-228.
[18]Martis RJ,Chakraborty C,Ray AK.Waveletbased Machine Learning Techniques for ECGSignal Analysis[M].Berlin:Springer,Machine Learning in Healthcare Informatics,2014:25-45.
[19]Terkelsen CJ,Norgaard BL,Lassen JF,et al.Prehospital evaluation in ST-elevation myocardial infarction patients treated with primary Percutaneous coronary intervention[J].J Electrocardiol,2005,38(Suppl 4):187-192.
[20]谈剑诚,张天贶,查晴,等.区域性心电信息管理系统的实施与成效[J].中国数字医学,2012,7(2):107-108.
[21]郭丽娜.心电信息管理系统的应用与展望[J].中医药管理杂志,2016,24(22):20-21.
[2]任懋榆,王建民.临床医学信息学[M].北京:军事医学科学出版社,2002.
[3]冯艳,侯秀丽,娜仁花,等.心电网络信息管理系统的临床应用及进展[J].中国数字医学,2014,9(2):115-117.
[4]李中健,李世锋,申继红,等.心电图学系列讲座(一)-绪论[J].中国全科医学,2014,17(1):115-117.
[5]李炜.心电检查网络系统MEMRS在临床的应用[J].电测试,2015(17):152-153.
[6]陈卫平,刘喻,刁文凯,等.区域化心电解决方案的探索与研究[J].中国数字医学,2015,31(8):92-94.
[7]Wang Y,Doleschel S,Wunderlieh R,et al.AWearable Wireless ECG Monitoring System With Dynamic Transmission Power Control for LongTerra Homecare[J].J Med Syst,2015,39(3):35.
[8]Bosson N,Kaji AH,Niemann JT,et al.The Utility of Prehospital ECG Transmission in a Large EMS System[J].Prehospital Emergency Care,2015,19(4):496-503.
[9]Cho GY,Lee SJ,Lee TB.An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems[J].J Med Syst,2015,39(1):161.
[10]Lee SY,Hong JH,Hsleh CH,et al.Low-Power Wireless ECG acquisition and classification system for body sensor networks[J].IEEE JBiomed Health Inform,2015,19(1):236-246.
[11]刘松涛,高迎春,刘慧波,等.心电图远程网络传输应用的临床价值[J].医疗卫生装备,2014,35(4):135-36.
[12]王健红,陈竹,姜小兵,等.实施临床心电图网络信息管理系统的优势[J].中国循证心血管医学杂志,2015,7(4):501-504,507.
[13]王健红.心电图信息化管理系统的设计与应用[J].中国急救复苏与灾害医学杂志,2014,9(7)653-654.
[14]Adams GL,Camplea PT,Adams JM,et al.Effectiveness of prehospital wireless transmission of electrocardiograms to a cardiologist via hand-held device for patients with acute myocardial infarction(from the Timely Intervention in Myocardial Emergency,NorthEast Experience[TIME-NE])[J].Am J Cardiol,2006,98(9):l160-1164.
[15]皇甫立夏,陆宇宏,施琦,等.基于.NET的心电网络系统的设计与实现[J].中国医学装备,2018,15(10):103-105.
[16]Scalvini S,Zanlelli E,Martinelli G,et al.Cardiac event recording yields more diagnoses than 24-hour Holter monitoring in patients with palpitations[J].JTelemed Telecare,2005,11(suppl 1):14-16.
[17]Zhang F,Lian Y.QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks[J].IEEE Trans Biomed Circuits Systems,2009,3(4):220-228.
[18]Martis RJ,Chakraborty C,Ray AK.Waveletbased Machine Learning Techniques for ECGSignal Analysis[M].Berlin:Springer,Machine Learning in Healthcare Informatics,2014:25-45.
[19]Terkelsen CJ,Norgaard BL,Lassen JF,et al.Prehospital evaluation in ST-elevation myocardial infarction patients treated with primary Percutaneous coronary intervention[J].J Electrocardiol,2005,38(Suppl 4):187-192.
[20]谈剑诚,张天贶,查晴,等.区域性心电信息管理系统的实施与成效[J].中国数字医学,2012,7(2):107-108.
[21]郭丽娜.心电信息管理系统的应用与展望[J].中医药管理杂志,2016,24(22):20-21.