A parallel implementation of a multi-state Kalman filtering algorithm to detect ECG arrhythmias

Detecting arrhythmias from the electrocardiogram (EGG) is of great importance for the continued development of intelligent cardiovascular monitors (ICM). An ICM's main goal is to present to the clinician a ‘high-level’ analysis of the patient's condition (e.g., the patient is slightly hypovolemic) based upon ‘lowlevel’ physiologic signals (e.g., blood pressure, heart rate, etc.). This paper reports on a parallel implementation of a multi-state Kalman filtering algorithm, within a prototype ICM, to help detect ECG arrhythmias. Preliminary test results show that the parallel, multi-state implementation performed exactly as the original sequential version. Several different rhythm disturbances were correctly identified after 3–5 beats. We conclude that our parallel implementation of the multi-state Kalman filter provides a faster and still reliable means of accurately detecting ECG arrhythmias in real-time.

[1]  Michael Factor.  The process trellis architecture for real-time monitors , 1990, PPOPP '90.

[2]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[3]  P L Miller,et al.  DYNASCENE: an approach to computer-based intelligent cardiovascular monitoring using sequential clinical "scenes". , 1990, Methods of information in medicine.

[4]  S.R. Smith,et al.  A real-time multiprocessor system for acquisition of multichannel neural data , 1988, IEEE Transactions on Biomedical Engineering.

[5]  Dean F. Sittig,et al.  Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm. , 1989, Computer methods and programs in biomedicine.

[6]  M. von Spreckelsen,et al.  Estimation of single-evoked cerebral potentials by means of parametric modeling and Kalman filtering , 1988, IEEE Transactions on Biomedical Engineering.

[7]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[8]  Donald E. Gustafson,et al.  ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms , 1978, IEEE Transactions on Biomedical Engineering.

[9]  Perry L. Miller,et al.  Real-time data fusion in the intensive care unit , 1991, Computer.

[10]  A F Smith,et al.  Monitoring renal transplants: an application of the multiprocess Kalman filter. , 1983, Biometrics.

[11]  Dean F. Sittig,et al.  The Multi-Trellis Software Architecture and the Intelligent Cardiovascular Monitor , 1992, Methods of Information in Medicine.

[12]  Jose C. Principe,et al.  Hidra: A Hierarchical Instrument for Distributed Real-Time Analysis of Biological Signals , 1987, IEEE Transactions on Biomedical Engineering.

[13]  Thomas Kailath,et al.  A parallel architecture for Kalman filter measurement update and parameter estimation , 1986, Autom..

[14]  Michael E. Factor.  The process trellis software architecture for parallel, real-time monitors , 1991 .

[15]  G. C. Fox,et al.  Solving Problems on Concurrent Processors , 1988 .

[16]  Nicholas Carriero,et al.  Linda in context , 1989, CACM.

[17]  Avent Rk,et al.  A critical review of trend-detection methodologies for biomedical monitoring systems. , 1990 .

[18]  Dean F. Sittig,et al.  A parallel software architecture for building intelligent medical monitors , 1989, International journal of clinical monitoring and computing.

[19]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[20]  R A Heath,et al.  Detection of change in physiological measures using an adaptive Kalman filter algorithm. , 1984, Psychological bulletin.

[21]  D. F. Sittig,et al.  Parallel Multi-channel Biologic Signal Processing , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  N Niilo Saranummi.  Detection of trends in long term recordings of cardiovascular signals , 1982 .