ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms

Gustafson,; Willsky,; Wang, Hong; Lancaster,; Triebwasser,

doi:10.1109/tbme.1978.326260

Cited by 80 publications

(27 citation statements)

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“…The Kalman filtering algorithm has been succesfully applied in research applitions to the identification of persistet and transient electrocardiogram (ECG) rhythms [12], to the estimation of single-evoked potentials in the electroencephalogram (EEG) [13], to the detection of renal allograft rejection [14,15], to long-term blood pressure recordings [161, and to white blood cell counts and urinary flow [17]. We are currently building an expert monitoring system which utilizes Kalman filters for physiological trend detection and artifact rejection from multiple input data streams in real-time.…”

Section: Introductionmentioning

confidence: 99%

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

Sittig

Factor

1990

Computer Methods and Programs in Biomedicine

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Section: Introductionmentioning

confidence: 99%

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

Sittig

Factor

1990

Computer Methods and Programs in Biomedicine

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“…The random fluctuations in interbeat interval was shown in a shortterm record (GUSTAFSON et al, 1978;CLAY and DEHAAN, 1979). The pattern of R-R interval histogram obtained over 24 hr was skewed in many cases with rabbits (HAYASHI, 1981).…”

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confidence: 99%

Twenty-four-hour pattern of ventricular excitation rhythm in resting normal human subjects.

1983

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Using a biotelemetry system, the electrocardiogram (ECG) was continuously measured over 24 hr in a given environmental condition in 8 normal human subjects. The frequency of ventricular excitation rhythm (VER) was obtained from inverse of R-R interval of ECG. This study was undertaken to be informed of the variation of VER over 24 hr in resting normal subjects with different ages. The averaged frequency of VER per day (VERB) and its relative coefficient of variation (CV) ranged from 1.44 to 1.01 Hz (mean : 1.24 Hz) and 26 to 10% (mean : 20%) with age of 10 to 73 years (mean : 42 years). VERd and CV showed a tendency to decrease with age. The VERd histogram showed not a normal distribution but a multimodal distribution. Two or three large positive deviations, denoted as L, N, and H modal components, from the normal distribution curve were detected. The mode of L and H modal components was 1.01 and 1.37 Hz, respectively. The frequency distribution of L and H modal components changed with relation to a circadian rhythm and sleepwakefulness cycles. An hourly histogram of ventricular excitation rhythm (VERh) frequently showed an unimodal distribution and the relative coefficient of variation of VERh ranged from 7.9 to 13.3 % which was smaller than that of CV. The mode of VERh histogram changed in association with the circadian rhythm, the sleep-wakefulness cycles and a fluctuation period of 2 to 3 hr.

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“…however, that such models are appropriate only for a limited class of arrhythmias. For example, three-state Markov chains (I) and state-space formulations (2) have been used to model timing patterns for the most prominent ECG feature, the R wave. and successful classification algorithms have been developed based on them.…”

Section: Intr~ouctionmentioning

confidence: 99%

Stochastic Petri net modeling of wave sequences in cardiac arrhythmias

Chin

Willsky

1989

Computers and Biomedical Research

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ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms

Cited by 80 publications

References 10 publications

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

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

Twenty-four-hour pattern of ventricular excitation rhythm in resting normal human subjects.

Stochastic Petri net modeling of wave sequences in cardiac arrhythmias

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