Novelty Detection

Tarassenko, Lionel; Clifton, David A.; Bannister, P.; King, Steve; King, Dennis

doi:10.1002/9780470061626.shm183

Cited by 19 publications

(16 citation statements)

References 28 publications

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“…The first stage in constructing a model of normality for novelty detection usually consists of obtaining more insight into the structure of the data [22]. Procedures for visualisation of the data in their original high-dimensional space are therefore required.…”

Section: Methodsmentioning

confidence: 99%

Modelling physiological deterioration in post-operative patient vital-sign data

Pimentel

Clifton

et al. 2013

Med Biol Eng Comput

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Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events.

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Section: Methodsmentioning

confidence: 99%

Modelling physiological deterioration in post-operative patient vital-sign data

Pimentel

Clifton

et al. 2013

Med Biol Eng Comput

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“…Consequently, novelty detection has been extensively applied in many research areas. Examples include medical diagnostic problems [5], failure detection in complex industrial problems [6], sensor networks [7], video surveillance [8], and detection of masses in mammograms [9]. Owing to its applicability and importance, various methods of novelty detection have been studied such as probabilistic, distance-based, domain-based, reconstruction-based, and information theoretic method [3].…”

Section: Introductionmentioning

confidence: 99%

http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=108&id=1140

Yoda¹,

Imakura²,

Matsuda³

et al. 2020

IJMLC

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Novelty detection represents the detection of anomalous data based on a training set consisting of only the normal data. In this study, we propose a new probabilistic approach for novelty detection to effectively detect anomalous data, particularly for the case of multimodal training dataset. Our method is inspired by the Least-Squares Probabilistic Classifier (LSPC), which is an efficient multi-class classification method. Numerical experimental results based on multimodal datasets show that the proposed method outperforms the related methods.

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“…Other examples of anomaly detection methodologies enable to model the dependencies of the structural responses on the environmental condition data such as temperature, wind speed, and traffic intensity loading. Other methods labelled as novelty detection are identifying anomalies using hypothesis testing approaches. Again, the main limitation of such approaches is that they do not include the prior probability of anomalies, the anomaly kinematic model, or the probability to transition from a normal to an abnormal state.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection with the Switching Kalman Filter for structural health monitoring

Nguyen

Goulet

2018

Struct Control Health Monit

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Summary The detection of changes in structural behaviour over time, that is, anomalies, is an important aspect in structural safety analysis. This paper proposes an anomaly detection method that combines the existing Bayesian Dynamic Linear Models framework with the Switching Kalman Filter theory. The key aspect of this method is its capacity to detect anomalies based on the prior probability of an anomaly, a generic anomaly model, as well as transition probabilities between a normal and an abnormal state. Moreover, the approach operates in a semisupervised setup where normal and abnormal state labels are not required to train the model. The potential of the new method is illustrated on the displacement data recorded on a dam in Canada. The results show that the approach succeeded in identifying the anomaly caused by refection work, without triggering any false alarm. It also provided the specific information about the dam's health and conditions.

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Novelty Detection

Cited by 19 publications

References 28 publications

Modelling physiological deterioration in post-operative patient vital-sign data

Modelling physiological deterioration in post-operative patient vital-sign data

http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=108&id=1140

Anomaly detection with the Switching Kalman Filter for structural health monitoring

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