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Fault Diagnosis in Highly Dependable Medical Wearable Systems

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Abstract

High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

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Notes

  1. Portuguese acronym for integrated cardiovascular surveillance system.

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Acknowledgments

This work has been financed by the ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project SIVIC PTDC/EEI-ELC/1838/2012 (FCOMP-01-0124-FEDER-028937), and grant contract SFRH/BD/81476/2011 (first author).

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Authors and Affiliations

  1. INESC TEC, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Cristina C. Oliveira & José Machado da Silva

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  1. Cristina C. Oliveira
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  2. José Machado da Silva
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Correspondence to Cristina C. Oliveira.

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Responsible Editors: G. Léger and C. Wegener.

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Oliveira, C.C., da Silva, J.M. Fault Diagnosis in Highly Dependable Medical Wearable Systems. J Electron Test 32, 467–479 (2016). https://doi.org/10.1007/s10836-016-5602-4

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  • DOI: https://doi.org/10.1007/s10836-016-5602-4

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