Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm

Sittig, Dean F.; Factor, Michael

doi:10.1016/0169-2607(90)90026-6

Cited by 35 publications

(12 citation statements)

References 21 publications

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“…Beat detection from the ECG is the most direct method for HR measurement (Kohler et al 2002), but since ICU noise and artifact are so prevalent, it is difficult for clinicians to believe the monitors' estimates without visual confirmation. Various strategies have been employed to improve estimates of noisy physiological parameters, such as averaging (Jakob et al 2000), machine learning (Tsien et al 2001), Kalman filtering (Sittig and Factor 1990, 2003 and signal quality assessment techniques (Allen and Murray 1996, Kaiser and Findeis 2000, Zong et al 2004, Chen et al 2006. Averaging methods can reduce the influence of transient artifacts, but at the cost of smoothing true physiologic changes.…”

Section: Introductionmentioning

confidence: 99%

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

2007

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Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.

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Section: Introductionmentioning

confidence: 99%

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

2007

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“…Similarly, traditional numeric methods to detect and identify temporal developments such as regression analysis, adaptive forecasting and Kalman filters [22][23][24][25][26][27][28], also rely on a minimum number of available samples. This is particularly difficult in a prospective application where a diagnosis must be made after each sample including the first, a situation for which none of the statistical methods is designed.…”

Section: Other Methods In the Fieldmentioning

confidence: 99%

A Method to Derive the Time of Onset of Infection from Serological Findings

Steimann¹,

Hayde²

1997

Methods Inf Med

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Abstract:Among the diagnostic problems requiring a retrospective assessment of the time an event occurred is that of screening for primary infection with Toxoplasma gondii acquired during pregnancy. We suggest a method to derive the possible times of onset of infection from a small sequence of serological samples by matching them against the knowledge about possible courses of infection. Special care is taken to properly address the relative change of consecutive samples, a nontrivial problem when reasoning about sparsely sampled time courses. To investigate the practicability of our approach we conducted a retrospective and a simulated prospective evaluation based on the samples of 394 pregnancies, randomly selected from our toxoplasmosis database; we could demonstrate an overall accuracy of 95.7%.

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“…Our initial version of the IeM was a high-level monitor [3]; it was designed to receive write probes containing error-free, numeric data from an external source once a second. This program contained 104 processes which performed a wide range of tasks including diagnosis [24] and trend detection [25]. While further work is necessary before exhaustive clinical testing, the IeM ran and has received a small amount of off-line testing using data recorded during clinical cases [26].…”

Section: The Extended Multi-trellis Iemmentioning

confidence: 99%

The Multi-Trellis Software Architecture and the Intelligent Cardiovascular Monitor

Gelernter¹,

Factor²,

Sittig³

1992

Methods Inf Med

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Abstract:A real-time, intelligent cardiovascular monitor is complex. It must process multiple waveforms, recognize artifacts, extract pertinent parameters, recognize a patient’s clinical state, analyze the problem and formulate a response. This paper presents the multi-trellis (a collection of process trellises), a software architecture for building such a monitor. A process trellis is a uniform hierarchical framework for heterogeneous program modules. The multi-trellis extension allows one to compile several process trellis programs with widely varying run-time requirements into a single executable program that it is efficient, predictable and usable. Our prototype consists of two process trellises. The lower trellis contains processes to analyze three different analog signals: the blood pressure from a non-invasive monitor and an arterial catheter, and the ECG. The upper trellis contains processes to help detect evolving hemodynamic trends, identify abnormalities, and present a succinct summary to the clinician. Our prototype shows that the multi-trellis is a demonstrably useful software architecture for building these real-time, intelligent monitors.

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Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm

Cited by 35 publications

References 21 publications

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

A Method to Derive the Time of Onset of Infection from Serological Findings

The Multi-Trellis Software Architecture and the Intelligent Cardiovascular Monitor

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