A parallel implementation of a multi-state Kalman filtering algorithm to detect ECG arrhythmias.
Detecting arrhythmias from the electrocardiogram (ECG) 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 'low-level' 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.
Sittig
D F
DF
Department of Anesthesiology, Yale University School of Medicine, New Haven, CT 06510.
Cheung
K H
KH
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D002319:Cardiovascular System / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D006801:Humans; D008954:Models, Biological; D008991:Monitoring, Physiologic; D012984:Software