Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a bayesian filter

Sayadi, O ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TBME.2010.2093898
  3. Abstract:
  4. In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover, we propose a measure of signal fidelity by monitoring the covariance matrix of the innovation signals throughout the filtering procedure. Five categories of life-threatening arrhythmias were verified by simultaneously tracking the signal fidelity and the polar representation of the CV signal estimations. We analyzed data from Physiobank multiparameter databases (MIMIC I and II). Performance evaluation results demonstrated that the sensitivity of the detection ranges over 93.50 and 100.00. In particular, the addition of more CV signals improved the positive predictivity of the proposed method to 99.27 for the total arrhythmic types. The method was also used for false arrhythmia suppression issued by ICU monitors, with an overall false suppression rate reduced from 42.3 to 9.9. In addition, false critical ECG arrhythmia alarm rates were found to be, on average, 42.3, with individual rates varying between 16.7 and 86.5. The results illustrate that the method can contribute to, and enhance the performance of clinical life-threatening arrhythmia detection
  5. Keywords:
  6. Arrhythmia verification ; Cardiovascular (CV) signals ; Extended Kalman filter (EKF) ; False alarm (FA) suppression ; Joint dynamical model ; Alarm rate ; Angular frequencies ; Arrhythmia detection ; Bayesian filters ; Detection range ; Dimensionality reduction ; Dynamical model ; False alarms ; Filter structures ; Filtering procedures ; Gaussian Mixture Model ; Heart rates ; Limit cycle ; Linear assignment ; Multiparameters ; Nonlinear joints ; Performance evaluation ; Phase parameters ; Polar representation ; Predictivity ; Signal estimation ; Signal fidelity ; Covariance matrix ; Equations of motion ; Kalman filters ; Nonlinear equations ; Ordinary differential equations ; Signal detection ; Bayesian learning ; Cardiovascular system ; Critically ill patient ; Extended Kalman filter ; Filter ; Heart arrhythmia ; Heart cycle ; Heart rate ; Hospital patient ; Human ; Intensive care unit ; Mathematical model ; Sedation ; Algorithms ; Arrhythmias, cardiac ; Bayes theorem ; Blood pressure ; Electrocardiography ; Heart arrest ; Humans ; Intensive care units ; Models, cardiovascular ; Photoplethysmography ; Pulse ; Sensitivity and specificity ; Signal processing, computer-assisted
  7. Source: IEEE Transactions on Biomedical Engineering ; Volume 58, Issue 10 PART 1 , 2011 , Pages 2748-2757 ; 00189294 (ISSN)
  8. URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5948364&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F10%2F4359967%2F05948364.pdf%3Farnumber%3D5948364