Fracture prediction of cardiac lead medical devices using Bayesian networks

Tarek Haddad, Adam Himes and Michael Campbell

Reliability Engineering and System Safety, 2014, vol. 123, issue C, 145-157

Abstract: A novel Bayesian network methodology has been developed to enable the prediction of fatigue fracture of cardiac lead medical devices. The methodology integrates in-vivo device loading measurements, patient demographics, patient activity level, in-vitro fatigue strength measurements, and cumulative damage modeling techniques. Many plausible combinations of these variables can be simulated within a Bayesian network framework to generate a family of fatigue fracture survival curves, enabling sensitivity analyses and the construction of confidence bounds on reliability predictions.

Keywords: Reliability prediction; Bayesian; Fatigue; Pacemaker lead; Defibrillator lead; ICD lead (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:123:y:2014:i:c:p:145-157

DOI: 10.1016/j.ress.2013.11.005

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