期刊文献+

应用基于数据融合的心率估计抑制监护仪错误报警 被引量:4

Suppress False Alarms of ICU Monitors Using Heart Rate Estimation Based on Data Fusion
下载PDF
导出
摘要 目的:研究重症监护病人基于心电和血压信号数据融合的心率估计算法,抑制监护仪的错误报警。方法:分别从心电和血压信号计算逐搏心率,通过分析信号特征得出反映信号质量好坏的信号质量指数,应用卡尔曼滤波方法分别对基于心电和血压分析得出的心率进行最佳估计,用卡尔曼滤波的残差和信号质量指数作为权重系数进行心率数据融合。应用融合心率算法对美国麻省理工学院多参数智能重症监护数据库II中记录的监护仪产生的2584次严重心动过缓和严重心动过速报警数据重新分析,以期抑制监护仪的错误报警。结果:本算法对2584次报警数据分析,对真实报警的正确识别率为99.64%,对错误报警的抑制率为70.66%。结论:基于数据融合的心率估计可有效抑制监护仪的错误报警,提高报警的准确率。 Objective: To develop a heart rate (HR) estimation method based on data fusion of electrocardiogram (ECG) and arterial blood pressure (ABP) from intensive care unit (ICU) patients, and to suppress the false alarms oflCU monitors. Methods: Beat-by-beat HR was calculated separately from ECG and ABP. Signal quality indices (SQI) were obtained by analyzing characteristics of each waveform. HR from the ECG and ABP was tracked with a Kalman filter and weighted by the Kalman filter's residual error and SQI to perform the HR data fusion. This method was evaluated using 2584 episodes of extreme bradycardia and extreme tachycardia alarms in the Multi-parameter Intelligent Monitoring for Intensive Care Ⅱ database. Results: Our algorithm detected correctly 99.64% of the true alarms and suppressed 70.66% of the false alarms. Conclusions: The algorithm could suppress false alarms oflCU monitors efficiently and improve the accuracy rate of alarms.
出处 《中国医学物理学杂志》 CSCD 2008年第3期676-678,共3页 Chinese Journal of Medical Physics
关键词 心率估计 数据融合 重症监护 错误报警 heart rate estimation data fusion ICU monitor false alarm
  • 相关文献

参考文献8

  • 1Tsien CL Fackler JC. Poor prognosis for existing monitors in the intensive care unit [J]. Crit Care Med, 1997,25(4):614-619.
  • 2Saeed M, Lieu C, Raber G, et al. MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring [J]. Comput in Cardiol, 2002,29:641-644.
  • 3Hamilton PS, Tompkins WJ. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database [J]. IEEE Trans on BME, 1986,33(12):1157-1165,
  • 4李桥,俞梦孙.重症监护病人心电导联信号质量评估[J].山东大学学报(医学版),2007,45(9):868-872. 被引量:7
  • 5李桥,Roger G Mark,Gari D Clifford,俞梦孙.基于信号质量评估和卡尔曼滤波的心率估计算法[J].中国医学物理学杂志,2007,24(6):454-457. 被引量:8
  • 6Zong W, Hddt T, Moody G B, et al. An open-source algorithm to detect onset of arterial blood pressure pulses [J]. Comput. in Cardiol. 2003,30:259-62.
  • 7Zong W, Moody G B, Mark R G. Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure [J]. Med. Biol. Eng. Comput, 2004,42(5):698-706.
  • 8Tarasseuko L, Mason L, Town,rid N. Multi-sensor fusion for robust computation of breathing rate [J].Electronics Letters, 2002,38(22): 1314-16.

二级参考文献22

  • 1Tsien C L,Fackler J C.Poor prognosis for existing monitors in the intensive care unit[J].Crit Care Med,1997,25(4):614-619.
  • 2He T,Clifford G D,Tarassenko L.Application of independent component analysis in removing artifacts from the electrocardiogram[J].Neural Comput Applic,2006,15(2):105-116.
  • 3Elena M M,Quero J M,Borrego I.An optimal technique for ECG noise reduction in real time applications[ J ].Comput Cardiol,2006,33:225-228.
  • 4Ortolani O,Conti A,Filippo A,et al.EEG signal processing in anaesthesia.Use of a neural network technique for monitoring depth of anaesthesia[J].Br J Anaesth,2002,88(5):644-648.
  • 5Saeed M,Lieu C,Raber G,et al.MIMIC Ⅱ:a massive temporal ICU patient datahase to support research in intelligent patient monitoring[J].Comput Cardiol,2002,29:641-644.
  • 6Friesen G M,Jannett T C,Jadallah M A,et al.A comparison of the noise sensitivity of nine QRS detection algorithms[J].IEEE Trans Biomed Eng,1990,37(8):85-98.
  • 7Hamilton P S,Tompkins W J.Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia datahase[J].IEEE Trans BME,1986,33(12):1157-1165.
  • 8Zong W,Moody G B,Jiang D.A robust open-source algorithm to detect onset and duration of QRS complexes[ J ].Comput Cardiol,2003,30:737-740.
  • 9Clifford G D,Azuaje F,McSharry P E.Advanced methods and tools for ECG data analysis[ M ].Norwood:Artech house,2006.
  • 10Moody G B,Muldrow W E,Mark R G.A noise stress test for arrhythmia detectors[J].Comput Cardiol,1984,11:381-384.

共引文献11

同被引文献52

  • 1http://www.physiónet.org.
  • 2Moody G B,Mark R G and Goldberger A L.PhysioNet:A web-based resource for the study of physiologic signals[J].Engineering in Medicine and Biology,IEEE,2001,20(3):70-75.
  • 3Costa M,Moody G B,Henry I,et al.PhysioNet:an NIH research resource for complex signals[J].Journal of Electrocardiology,2003,36(Suppl):139-144.
  • 4Saeed M,Lieu C,Raber G,et al.MIMIC Ⅱ:a massive temporal ICU patient database to support research in intelligent patient monitoring[J].Comput in Cardiol,2002,29:641-644.
  • 5Tsien C L,Fackler J C.Poor prognosis for existing monitors in the intensive care unit[J].Crit Care Med,1997,25(4):614-619.
  • 6Drew BJ,Califf RM,Funk M,et al.Practice standards for electro-cardiographic monitoring in hospital settings[J].Circulation,2004,110(17):2721-2746.
  • 7Atzema C,Schull MJ,Borgundvaag B,et al.Alarmed:adverse events in low-risk patients with chestpain receiving continuous elect-rocardiographic monitoring in the emergency department:a pilot study[J].Am J Emerg Med,2006,24(1):62-67.
  • 8Chambrin MC,Ravaux P,Calvelo-Aros D,et al.Multicentric study of monitoring alarms in the adult intensive care unit(ICU):a descriptive analysis[J].Intensive Care Med,1999,25(12):1360-1366.
  • 9Chambrin MC.Alarms in the intensive care unit:how can the number of false alarms be reduced?[J].Crit Care,2001,5(4):184-188.
  • 10Siebig S,Kuhls S,Imhoff M,et al.Intensive care unit alarms-how many do we need[J].Crit Care Med,2010,38(2):451-456.

引证文献4

二级引证文献78

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部