False alarm suppression in early prediction of cardiac arrhythmia

Roychoudhury, Shoumik; Ghalwash, Mohamed; Obradović, Zoran

doi:10.1109/bibe.2015.7367628

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Roychoudhury et al ( 50 ), Wang et al ( 49 ) and Srivastava et al ( 61 ) use data from the MIMIC-II database ( 18 ) for detecting false arrhythmia alarms. Roychoudhury et al ( 50 ) developed a method for extracting shapelets from the ECG signal that are distinctive for false alarms concerning ASY and VT that can be matched for prediction while considering some uncertainty. The other two publications present classical machine learning (ML) systems.…”

Section: Resultsmentioning

confidence: 99%

“…This is the case in (43)(44)(45)(46)(47) Furthermore, the MIMIC-II database is an important material in alarm fatigue research. MIMIC-II is used by (36,(48)(49)(50)(51)(52)(53). It is, however, noteworthy that specifically MIMIC-II is used while its successor MIMIC-III (54) and other ICU databases such as HiRID (55) and eICU CRD (56) are not even mentioned in the publications under review.…”

Section: Datamentioning

confidence: 99%

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Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Chromik

Klopfenstein

Pfitzner

et al. 2022

Front. Digit. Health

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Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

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Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Chromik

Klopfenstein

Pfitzner

et al. 2022

Front. Digit. Health

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“…Another example in network security, the application associated with TCP flow can be identified at an early stage by observing the initial few packets of TCP traffic flow and as a result no need to observe the complete flow of TCP connection 3 . Also, early classification has demonstrated its usefulness in various domains as well as in applications such as early event detection, 4,5 health informatics 1,6 and process control in industries 7 …”

Section: Introductionmentioning

confidence: 99%

“…1,2 Another example in network security, the application associated with TCP flow can be identified at an early stage by observing the initial few packets of TCP traffic flow and as a result no need to observe the complete flow of TCP connection. 3 Also, early classification has demonstrated its usefulness in various domains as well as in applications such as early event detection, 4,5 health informatics 1,6 and process control in industries. 7 In recent years, many extensions of conventional classification problems for sequence data have been explored such as early classification, [7][8][9][10][11][12] semisupervised classification, 13,14 and sequence to sequence classification.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for early malware detection

Sharma

Singh

2020

Trans Emerging Tel Tech

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Early classification of time series is valuable in many real‐world applications such as early disease prediction, early disaster prediction, and patient monitoring where data are generated over time. The main objective of early classification is to provide a reliable class prediction earliest in time. In general, whenever the early prediction time improves, the prediction accuracy decreases. Thus, the trade‐off between earliness and accuracy needs to be addressed. In this article, we proposed an optimization‐based early classification model for time series data using early stopping rules (ESRs) and a series of probabilistic classifiers. ESRs are developed through particle swarm optimization by minimizing the well‐defined cost function that considers the missclassification cost and delaying decision cost simultaneously. The experimental results on 30 standard datasets demonstrate good performance for early classification in comparison to state of the art methods. Also, the proposed model is tested for early malware detection on a real dataset and shows decent performance by balancing the accuracy and earliness.

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