Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm.
Using a parallel implementation of the multi-state Kalman filtering algorithm, we have developed an accurate method of reliably detecting and identifying trends, abrupt changes, and artifacts from multiple physiologic data streams in real-time. The Kalman filter algorithm was implemented within an innovative software architecture for parallel computation: a parallel process trellis. Examples, processed in real-time, of both simulated and actual data serve to illustrate the potential value of the Kalman filter as a tool in physiologic monitoring.
Sittig
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Department of Anesthesiology, Yale School of Medicine, New Haven, CT 06510.
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D000465:Algorithms; D005103:Expert Systems; D006801:Humans; D008991:Monitoring, Physiologic; D012815:Signal Processing, Computer-Assisted